DeepSig

Deep Learning-based Mutational Signature Caller for MSK-IMPACT Data

Overview

DeepSig contains single-base substitution (SBS) mutational signature inference tools intended for data sets derived from targeted sequencing (MSK-IMPACT) platforms. Models for major cancer types, pre-trained using synthetic data derived from whole-genome seqeuencing (WGS) mutational catalogs and matched to corresponding MSK-IMPACT data, are used to predict presence of all reference signatures in a new clinical sample based on estimated precision.

For a single sample or a small cohort derived from MSK-IMPACT, the main workflow comprises extracting signature scores using a cancer type-specific model, making discrete ternary calls, and estimating exposures (number of mutations attributed to each signature).

Input data

Input data are of the form of catalog matrix:

Sample_ID	A[C>A]A	A[C>A]C	A[C>A]G	A[C>A]T	C[C>A]A
Tumor_Sample_Barcode_1	0	3	5	0	0
Tumor_Sample_Barcode_2	2	1	1	2	0
Tumor_Sample_Barcode_3	5	0	0	1	1

Column names are the the set of categories to which mutation data from sequencing experiments have been classified (Sample_ID column name is absent in the actual file). These trinucleotide contexts are the pyrimidine bases before and after mutation flanked by upstream and downstream nucleotides (96 in total). Each row corresponding to Tumor_Sample_Barcode contains non-negative counts of single nucleotide variants (SNVs). This trinucleotide matrix can be generated using the utility function maf2cat3. MAF files can be generated from VCF files using vcf2maf. Note: the output from [maf2cat3] needs to be transposed so that rows contain samples.

Main caller

The function

DL.call(catalog, cancer.type = 'pancancer', model.path = './.DeepSig', min.attr = 1, ...)

will find the pre-trained model corresponding to cancer.type and perform signature fitting and filtering. The cancer.type argument is used to choose among pre-trained models:

Cancer type	cancer.type	OncoTree	ICGC/PCAWG	TCGA
Breat cancer	breast	Breast	Breast	BRCA
Ovarian cancer	ovary	Ovary/Fallopian Tube	Ovary	OV
Prostate cancer	prostate	Prostate	Prostate	PRAD
Pancreatic cancer	pancreas	Pancreas	Pancreas	PAAD
Bladder cancer	bladder	Bladder/Urinary Tract	Bladder	BLCA
Colorectal cancer	colorect	Bowel	Colorectal	COAD
Melanoma	skin	Skin	Skin	SKCM
Glioma	cns	CNS/Brain	CNS	GBM
Non-small cell lung cancer	nsclc	Lung	Lung	LUAD/LUSC
Small cell lung cancer	sclc	CSCLC
Head and neck cancer	head_neck	Head and Neck	Head	HNSC
Renal cell carcinoma	kidney	Kidney	Kidney	KICH/KIRC/KIRP
Endometrial cancer	uterus	Uterus	Uterus	UCEC
Esophagogastric cancer	esophagus	Esophagus/Stomach	Esophagus	ESCA/STAD
Germ cell tumor	gct			TGCT
Pan-cancer model	pancancer

Input argument cancer.type will be checked against the second column above. If it does not match one, it will be thought of as an oncoTree code, and an attempt will be made to match it to cancer.type using oncoTree(). This matching of oncotree code to cancer.type is mostly based on the top-level tissue name of oncotree hierarchy, except for germ cell tumor and sclc. If cancer.type does not match an oncotree code, it will be thought of as a custom model (see below).

In general, the choice of which model to apply for a cohort should be based on the knowledge of how similar the cohort is to one of the tissue of origin-based cancer types above. For rare cancer types, cancer of unknown primary, and samples for which signatures not in the reference might be expected (e.g., lung cancer samples with history of treatment with temozolomide or hypermutation with POLE oncogenic mutations), pancancer model can be used. The drawback of pancacer model is that detection precision and recall are generally lower than cancer type-specific models.

To reduce the size of package, default models are not included in the package. The model.path argument (by default ./.DeepSig) is the path where trained models can be found: directory containing SBS* subdirectories, similar to those in inst/extdata/dlsig/v0.95/breast, and other associated files (refsig.txt and threshold_cut.txt) will be looked for in model.path/[cancer.type] subdirectory. The table of reference signatures (refsig.txt) in a model can be looked up from pre-trained model directories (e.g., for breast cancer, see inst/extdata/dlsig/v0.95/breast). If this main subdirectory does not exist, an attempt will be made to download these files to the directory via queries to GitHub REST API with modelFetch(). This scheme also ensures that a second call for the same cancer.type will use the previously downloaded data. Runs without internet access will require pre-downloaded models whose path is provided as model.path.

As an example, the following script will generate outputs for the TCGA-OV samples using ovary pre-trained model:

> library(DeepSig)
> xcat <- read.table(system.file('extdata', 'tcga_ov_xcat.txt', package = 'DeepSig'), header = TRUE, sep = '\t')
> z <- DL.call(catalog = t(xcat), cancer.type = 'ovary')
> names(z)
  [1] "score"        "ternary.call" "exposure"     "ref.sig"      "threshold"

Output

The return value is a list of 5 data frames as shown in the above example. The component score contains continuous scores for the presence of each sample-signature combination:

sid	M	SBS1	SBS2.13	SBS3	SBS5	SBS6
TCGA.61.2092	141	5.24	2.72	9.03	2.67	-4195
TCGA.36.2540	10	2.93	-1.39	1.57	1.57	-73
TCGA.24.1467	9	-0.09	-1.44	-0.50	-0.50	-849

where the columns show the repertore of reference signatures present in the ovary model being used. The columnn labeled M shows the total number of mutations for each sample.

The threshold values accessbile with threshold are input data containing two threshold scores for minimum estimated precision and minimum negative predictive value (NPV), both of 0.9 for most cases, shown in column min_value:

S	measure	min_value	threshold	precision	recall	npv	engine
SBS1	precision	0.9	1.90	0.90	0.09	0.73	mlp
SBS1	npv	0.9	-2.55	0.29	0.99	0.90	mlp
SBS2.13	precision	0.9	1.22	0.90	0.19	0.77	mlp
SBS2.13	npv	0.9	-1.43	0.38	0.85	0.90	mlp
SBS3	precision	0.9	0.92	0.90	0.85	0.62	mlp
SBS3	npv	0.9	-1.24	0.77	1.00	0.90	mlp

The columns precision, recall, and npv show the values attained at the thresholds. The column engine is informational only: either mlp or cnn, the flavor of DL engines that was used for optimal performance in training.

These threshold values are used to clip signature scores into discrete calls as shown in ternary.call:

sid	M	SBS1	SBS2.13	SBS3
TCGA.61.2092	141	P	P	P
TCGA.36.2540	10	P	I	P
TCGA.24.1467	9	I	N	I

There are three types of calls:

• P (positive): signature is present with minimum precision of 0.9
• N (negative): signature is not present with minimum NPV of 0.9
• I (indeterminate): signature is neither present with minimum precision of 0.9 nor absent with mininum NPV of 0.9

The component exposure is a data frame of exposure proportions for all signatures with either P or I calls:

sid	M	SBS1	SBS2.13	SBS3
TCGA.61.2092	141	0.18	0.05	0.18
TCGA.36.2540	10	0.82	0	0.17
TCGA.24.1467	9	0.52	0	0

These proportions are obtained by fitting the subset of signatures with P or I calls to each catalog using extractSig, which uses maximum likelihood estimation. Additionally, any proportions that lead to attribution (proportion times M) less than min.attr parameter to DL.call (default 1) are set to zero.

The ref.sig is informational only since it has been used in training the model and refitting:

Mutation types	SBS1	SBS3	SBS2.13	SBS8
A[C>A]A	0.027	0.014	0.004	0.02
A[C>A]C	0.013	0.010	0.002	0.01
A[C>A]G	0.004	0.002	0.001	0.003

The reference signatures are primarily based on existing databases of WGS-derived signatures including signal.

Standalone script

If you are not interested in interactive usages with more flexibility and functionality, or want to use DeepSig as a part of a pipeline, use the command-line script deepsig.R. If you installed DeepSig as an R package using install_github, find the path via

system.file('exec', 'deepsig.R', package = 'deepsig')

If you cloned the repository, the file is located at the ./exec subdirectory of the github main directory. We denote this package directory path as PKG_PATH. The command syntax is

$ $PKG_PATH/exec/DeepSig.R -h
usage: ./deepsig.R [-h] -i CATALOG -o OUTPUT -c CANCER_TYPE [-q]

Extract mutational signatures using DeepSig algorithm

options:
  -h, --help            show this help message and exit
  -i CATALOG, --input CATALOG
                        input catalog data file
  -o OUTPUT, --output OUTPUT
                        output directory
  -c CANCER_TYPE, --cancer-type CANCER_TYPE
                        use a model for the specified cancer type
  -q, --quiet           Run quietly

The arguments CATALOG, CANCER_TYPER, and OUTPUTspecify the paths of input catalog data, cancer type, and output directory.

Installation

Compilation requires GNU Scientific Library (GSL). In Ubuntu Linux,

$ sudo apt-get install libgsl-dev

On a Mac,

$ brew install gsl

Also set

PKG_CPPFLAGS = -I/opt/homebrew/Cellar/gsl/2.7.1/include
PKG_LIBS = -L/opt/homebrew/Cellar/gsl/2.7.1/lib -lgsl -lgslcblas

in ~/.R/MAKEVARS on a Mac (check the links to make sure the correct version number is used).

DL-based filtering requires tensorflow and pandas. In a Mac,

$ python3 -m pip install pandas h5py tensorflow==2.15.0

See the tensorflow install page for further information.

We currently recommend downloading a source tar.gz file and installing DeepSig via

> install.packages('DeepSig_v0.9.8.tar.gz', repos = NULL, dependencies = TRUE)

which will also install dependencies.

Catalog matrix generation

The catalog file as input data can be generated from MAF file using maf2cat3(). It requires a reference genome package, either BSgenome.Hsapiens.UCSC.hg19 or BSgenome.Hsapiens.UCSC.hg38 installed:

> library(BSgenome.Hsapiens.UCSC.hg19)
> maf <- system.file('extdata', 'brca.maf', package = 'DeepSig')
> x <- maf2cat3(maf = maf, ref.genome = BSgenome.Hsapiens.UCSC.hg19)
> write.table(x, file = 'brca_catalog.txt', row.names = TRUE, col.names = TRUE, sep = '\t', quote = F)

If you do not want to use R-interface, a command-line script is available, assuming that Bsgenome.Hsapiens.UCSC.hg19 package has been installed:

$ ./maf2cat3.R -h
usage: ./maf2cat3.R [-h] MAF CATALOG

Construct mutational catalog from MAF file with Ref_Tri column

positional arguments:
  MAF         input MAF file
  CATALOG     output catalog file

optional arguments:
  -h, --help  show this help message and exit

De novo inference

See the vignettes