Peer review of: The mutREAD method detects mutational signatures from low quantities of cancer DNA (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): Expert in mutational signatures 

The manuscript by Perner J et al describes an elegant, novel, cost effective and scalable method 
allowing extracting SNV-based mutational signatures from reduced representation of the genome 
material and from lower genome-wide coverage sequencing data. This interesting novel tool appears 
to have a lot of potential for use in more streamlined signature analyses in various settings, clinical, 
experimental etc. 

The manuscript is well written, concise, and the techniques – laboratory ones as well as computational 
- are described clearly. 

I have the following several comments: 

Major: 
1) It appears that the signature analysis relies on SNVs that were called by using only a single caller, 
MuTect2, which for various reasons might not be everyone’s choice. Given the atypical nature of the 
input material due to reduced content and/or sample quality or low amounts, it would be interesting to 
compare the MuTect2 performance with other typical callers currently used/recommended for instance 
by the TCGA or ICGC consortia. Could the authors address this more systematically in the Manuscript 
by describing the consensus/union and unique outputs of 2-3 well-tested callers applied to the 
mutREAD data, and provide some recommendations/conclusions as to the impact of such multi-caller 
approach on the overall signature extraction results? 

2) Most of the signatures seen in EACs (sig 1, 2/13, 17 and possibly 18 but not 3) have quite distinct 
and rather spiky profiles, and this could improve the reliability of their detection in the reduced 
content data. Could the authors test by for instance computational simulation (I am thinking using 
random selection of mutations corresponding to genome-wide coverage typically obtained by 
mutREAD) another data set that has more diverse signature content, for example a comparably sized 
liver HCC WGS data set from the ICGC data, to see if the reconstitution of the existing signature 
assignments and per-sample proportional distributions is as successful as seen in the EAC samples? 
Such exercise, if feasible, would possibly allow for making broader general statement about the 
mutREAD’s utility and effectiveness. 

Minor: 
3) The concordance of the proportionate signature distribution between WGS and mutREAD within-
sample shown in Figs 1D, 2A is impressive, attesting to the utility of this new approach. Unless I 
missed the information elsewhere in the manuscript, it would be helpful to also see how the matching 
techniques compare at the level of the SNV counts that are being compared. Could the authors include 
this information for each of the compared tumors, ideally by also showing in the same figures bar 
graphs based on the counts, or if this happens to be visually confusing due to for example displaying 
very high versus very low counts side by side, at least annotate the samples with the corresponding 
count numbers? 

Reviewer #2 (Remarks to the Author): Expert in sequencing technologies and clinical genetics 

In this study entitled “A cost-effective assay for quantifying mutational signatures in clinical cancer 
samples” by Perner et al, the authors presented the development of a sequencing and computational 

method for the determination of mutational signatures in DNA samples. They showed their method, 
termed as mutREAD, mirrored the WGS-derived mutational signatures. Importantly, the method can 
be applied to DNA extracted from the FFPE samples with low tumor content. This work is meaningful 
with potential for applications. 
The reviewer is not an expert on computational biology and thus would not comment on the 
computational technicality of the manuscript. But from the perspective of cancer genomics, the 
concerns are listed below: 
1. The computational simulations were conducted using available data from WGS of 129 esophageal 
adenocarcinoma tumor samples. If the authors claim the method may have a wide range of 
applications, they will need to demonstrate it works not only on a type of solid tumor, but also on a 
type of leukemia and/or a type of lymphoma. For example, they may want to test mutREAD in 
lymphoma mutational signatures described in Chapuy, Nat Med, 24, 679, 2018. 
2. Besides SNVs, does the method apply to copy number variations and structural abnormalities such 
as translocations and inversions etc? 
3. If it is only demonstrated in the setting of esophageal adenocarcinoma, the scope of the ms would 
be quite limited and the ms may fit better to a journal with special focus on cancer computational 
genomics or a journal with special focus on clinical applicability such as Journal of Molecular 
Diagnostics.