Science - USA (2022-04-22)

detectable using WGS signatures but for which
a genetic driver cannot be identified. It is
noteworthy that a causal genetic event could
not be detected for one in two cases with MMRd
and one in two cases with HR deficiency,
indicating that signature analysis is more
sensitive for identifying these defects than ex-
amination of mutations in selected genes. Fur-
thermore, an agnostic WGS approach to
tumor characterization will help reveal abnor-
malities that we currently neither seek nor
detect using customary diagnostic pathways.
For example, we found MMRd-associated sig-
natures with a frequency of <1% in many tumor
types, including stomach, prostate, pancreas,
ovary, NET, lung, kidney, oropharyngeal, CNS,
breast, sarcoma, and bladder cancers. Given
the reported therapeutic relationships between
MMRd phenotypes and immune checkpoint in-
hibitors ( 37 Ð 39 ), from a personalized pan-cancer
therapeutics perspective, many of these patients
maybeeligiblefortreatmentoptionsthatwould
otherwise not be available to them.
At present, many of the previously un-
reported signatures have no known etiology.

This is not surprising because of the com-

plexity of drawing causal relationships, partic-

ularly for endogenous signatures, which can

be the outcome of multiple co-occurring events.

For example, a gene defect inMBD4could

convert the ubiquitous C>T at CpG into a

hypermutator phenotype (SBS96), or a patho-

physiological state such as replication stress

could amplify APOBEC-related SBS13. Some

endogenous signatures may only manifest as

part of an adaptive response to stressful stim-

uli. For example, SBS17, defined by T>G and

T>C mutations, was reported in mouse cells

that have been through immortalization, in

normal human cells treated with 5-FU ( 40 ),

and in a wide variety of cancers. Thus, many of

the signatures of unknown etiology could be

due to not just a single-gene defect but multi-

gene or complex pathway abnormalities ( 41 )

and/or may become overt after an adaptive re-

sponse to cellular stress. Further work will be

required to fully comprehend the causes of

many cancer mutational signatures.

As our knowledge base increases, the com-

plexity of assigning genetic causality to signa-

tures is evident in examples such as theOGG1

polymorphic risk allele, for which some patients

exhibit SBS108 clearly but others do not. Look-

ing forward, alternative strategies may be

needed to detect the contribution of moderate-

and lower-penetrance germline risk alleles to

somatic signatures in large cohorts.

Notably, the present analysis introduces the

concept of common versus rare signatures

within each tumor type. It highlights how an

increased number of samples may help dis-

cern common signatures that occur at low

levels for specific tumor types. Greater sample

numbers may also help unveil signatures that

occur at a low frequency in the population.

Crucially, the availability of independent, open-

access datasets such as those from the ICGC

and HMF has been instrumental in corrobo-

rating these common and rare signatures

identified within the GEL dataset. Although it

is far simpler to discuss signatures as unifying

reference patterns across all organs, it is im-

portant to note that these are mathematical

reference patterns, an average of many extrac-

tions, and not necessarily an accurate biological

Degasperiet al.,Science 376 , eabl9283 (2022) 22 April 2022 12 of 15

STEP 1 STEP 2

Common

Signatures

Rare

Signatures

New CNS

cancer sample FitMS

Fit tumour-type specific

common signatures

Identify additional rare signatures

Breast cancer samples CNS cancer samples Colorectal cancer samples

Fig. 7. Illustration of common and rare mutational signatures in cancer
samples and the workflow of FitMS.Schematic depiction of common (gray and
lighter colors) versus rare signatures in three example tumor types (breast, CNS,
and colorectal cancers). Each patient could have different amounts of some (or all)
of the common signatures. Occasionally, a patient may carry a rare signature as
well (bright colors). Some common signatures are ubiquitous and present in nearly

all tumor types, whereas other common signatures are restricted to certain tumor

types. Rare signatures may be unique (yellow dot) or may also occur in other tumor

types(reddots).Weproposeapracticalpackage,FitMS,thatmakesuseofthe

insights obtained in this study. Given a new sample—for example, a new brain cancer

WGS mutation catalog—FitMS will fit common CNS signatures before attempting

to discover additional rare signatures seen in CNS and other tumors.

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