Peer review of: Reversal of pre-existing NGFR-driven tumor and immune therapy resistance (2020)


 
 
 

REVIEWER COMMENTS 

Reviewer #1 (Remarks to the Author): 

In the revised version of their manuscript, Boshuizen and colleagues have incorporated an 
extensive set of additional experiments, including CRISPR/Cas9-mediated NGFR KOs and ectopic 
NGFR expression, which all in all considerably strengthened their study. Together with some new 
mechanistic assays, the experiments nicely support their finding that NGFR confers T cell 
resistance to melanoma cells in vitro. 
It would have even further increased the impact of the paper if the authors had addressed whether 
NGFR inhibition can prevent melanoma resistance formation in vivo or, vice versa, whether NGFR 
overexpression in parental melanoma cells is sufficient to drive resistance in vivo. In any case, I 
feel that the study is now acceptable for publication. However, I suggest to clarify, for instance in 
the Discussion, line 376, that “ectopic expression of NGFR conferred T cell resistance to parental 
cells IN VITRO” or: “…to parental cells, at least in cell culture”. 
Furthermore, I feel that the title is not very clear; the authors might want to consider rephrasing 
it. 

Reviewer #2 (Remarks to the Author): 

The authors did an excellent job in answering my comments. /Jonas Nilsson 

Reviewer #3 (Remarks to the Author): 

While I congratulate the authors for the present manuscript in which they use very complete in the 
array of techniques and analyses, the part of the study regarding the RNAseq experiment and 
analysis is quite lacking. While I understand that this manuscript is late in the review process, 
there are several issues regarding the RNAseq part that need to be addressed. 
For reasons there is this common line of thinking that in all cases PCR duplicates need not be 
removed for RNAseq data, which is not true at all. It all depends on the scenario: 
1) Amount of starting RNA: if there is enough starting RNA in your samples (~200ng or more), 
removing PCR duplicates does not have a big effect, and can even be detrimental depending on 
the read length. But if the starting material is scarce, not removing the PCR duplicates will incur 
into artificially inflated gene expression measures due to the low diversity of the starting library 
depending on the target number of reads sequenced. 
2) Length of reads and Paired End (PE) vs Single End (SE): in SE, the ability to discern PCR 
duplicates from reads that just look like a PCR duplicate but is not is seriously hampered. 
Similarly, the shorter the reads, the more difficult to discern true PCR duplicates is. The authors 
have performed SE 65bp read sequencing which is one read and very short (common practice for 
RNAseq is 75x2 and optimal is 150x2). Since the authors are in the low end (and only SE) they 
need to have really good starting material quality and amount to justify not removing PCR 
duplicates. 
3) Related to the last point, the length of read al so critically affects multimapping rate of reads, 
which when high, then also artificially inflate the expression levels of certain genes with high 
homology between them. 
Hence the authors need to provide a supplementary table with the RNA extraction metrics to 
discern whether it is ok to leave the PCR duplicates in or they need to be removed. This needs to 
be state din the M&M section as a sentence, specifying the average amount of RNA extracted from 
samples and also specifying whether PCR duplicates need to be removed or not. 
Furthermore, a supplementary table with STAR alignment metrics need to be provided. STAR 
outputs all these metrics as a normal part of its output. Critical metrics to show would be total raw 
reads, mapped reads, Total splices and break down stats for those splices, mismatch, deletion and 

insertion rates, multimaps and multimaps percentages as well as percentage of high multimap 
reads, percentage of reads that are too short, and finally total reads which are PCR duplicates and 
the percentage of those (whether they are removed or not, as stated above). 
Regarding the multimapping reads they also need to provide explanation as of they were handled 
(i.e. what threshold of percentage of multimaps was used to discard reads as “high multimap 
reads”) 
Lastly the authors mention that they have used the “default” settings of STAR. The default is single 
pass mode, which is far less accurate than the two pass mode of STAR. This needs to be specified 
in the M&M section, and if it is indeed the case that they have done single pass alignment with 
STAR, they need to provide and explanation as of why they have instead of using the two pass 
mode. 
All this information is critical to the reproducibility of the study and the evaluation of the quality of 
the analysis done in the RNAseq part of it.