Science - USA (2022-04-29)

to phenotypes is highlighted by our observa-
tion of how the effect of a given single muta-
tion depends on genetic context. For example,
CBTPA and CBTP3 melanocytes in vitro were
more similar in expression to CBTP melano-
cytes than to CBTA or CBT3 melanocytes, sug-
gesting thatPTENloss modulates the effect
ofAPCandTP53loss on gene expression. In

another example, aTP53mutation produced

different effects depending on which other

mutations were present: CBT3 melanocytes in

vivo did not form sizable primary tumors, but

CBTP3 tumors grew faster than CBTP tumors,

reflecting an interaction betweenPTENand

TP53mutations ( 68 ) (although no significant

association between these two mutations has

been noted in patient melanomas) ( 16 , 24 , 69 ).

These nonadditive effects also extend to the

tumor microenvironment. For example, the

abundance of neutrophils in CBTA and CBTP3

tumors and their absence or near absence in

CBTP and CBTPA tumors was not attributable

to a difference in a single mutation, as the

two tumor groups shared many mutations.

Hodiset al.,Science 376 , eabi8175 (2022) 29 April 2022 11 of 14

A

B

E

CD

Predict on patient-derived melanoma pathology slides (TCGA)

Classification

Genotype:

Prediction:

Tile prediction:

CBTPA

CBTPA

CBT

Unassigned

CBTP

CBTP3

Correct

CBTP CBTP3 CBTPA Unassigned

Unassigned Incorrect

True positive rate

Genotype

False positive rate Predicted genotype

1.0

1.0 CBTPA

0.8

0.8 CBTP3

0.6

0.6 CBTP

0.4

0.4 CBTA

0.2

0.2 CBT3

0.0

0.0 CBT

True positive rate

False positive rate

1.0

1.0

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0.0

0.0

False positive rate

0.0 0.2 0.4 0.6 0.8 1.0

False positive rate

0.0 0.2 0.4 0.6 0.8 1.0

Random (AUC 0.50)

CBT (AUC 0.99)

CBT3 (AUC 1.00)

CBTA (AUC 1.00)

CBTP (AUC 0.89)

CBTP3 (AUC 0.90)

CBTPA (AUC 1.00)

Random (AUC 0.50)

APC or CTNNB1 mutant

(AUC 0.58)

OxPhos (AUC 0.74)

Random (AUC 0.50)

TP53 mutant (AUC 0.52)

MITF-low or Common

(AUC 0.63)

Random (AUC 0.50)

PTEN mutant (AUC 0.53)

Predict on held-out melanoma models

Predict TP53 loss-of-function

p53-mutants or -associated expression programs

Predict PTEN loss-of-function

PTEN-mutants or -associated expression programs

Predict APC loss-of-function

Wnt-mutants or -associated expression programs

Confusion matrix

CBTPA CBTP3 CBTP CBTA CBT3 CBT 0

20

40

60

80

100

Percentage

of genotype’s

predictions (%)

(X) : # of

predictions

Mutant melanocyte

tumors

BBatch normalization RReLU SSoftmax

Tumor

section

Tile

2048 × 2048

Overlapping

patches

3 × 512 × 512

INPUT ENCODER

(pre-trained on ICIAR2018)

CNN ARCHITECTURE OUTPUT

(trained on xenograft tumor sections)

{CBT, CBT3, CBTA,

CBTP, CBTP3, CBTPA}

Predicted (tile)

Genotype

(probability vector)

Predicted (section)

Genotype

(probability vector)

Predicted (section)

Genotype label

S

Cross

entropy

Back

propag. TRAINING LABEL

Ground Truth (tile) Genotype Labels

3x 3x CBTA

(^64128256)
32
conv 3
×^3
2x
16
conv 2
×^2
conv 3
×^3
conv 2
×^2
conv^3
×^3
conv^2
×^2 conv 3 × 3 conv 3× 3
B
R
B
R
B
R
B
R
B
R
B
R
B
R
B
R conv 1
×^1
2x
2x
Feature
maps
64 × 64
2x
(^64128)
conv^3
×^3 conv 2 ×^2 conv 3 × 3
B
R
B
R
B
R
2x
conv 2 × 2 B
R conv 1
×^1
CBT • CBT3 • CBTA • CBTP • CBTP3 • CBTPA
Sum
vectors
Max
value
Fig. 6. Tumor genotype leads to distinct histological features that also
associate with genotype-linked expression states in patient melanomas.
(A) Computational approach to classify histological slides into engineered
genotypes. (B) Test set classification examples. Classification of individual tiles
(colored squares overlying tissue images), the aggregated classification for the entire
section (“prediction”), and the true genotype (“genotype”) for three examples.
(CandD) Successful prediction of genotype from histology in held-out mutant
melanocyte in vivo tumor section images. (C) Receiver operating characteristic
(ROC) curves of the prediction false positive rate (xaxis) and true positive rate
(yaxis) at each probability threshold for each genotype (color). The area under the
curve (AUC) is indicated for each genotype in the legend. (D) Percentage (color bar)
of samples from a given genotype (yaxis) that received each genotype classification
(xaxis). The percentage and number of such predictions are displayed within
each cell. (E) Inferring genotype and genotype-associated expression states in
patient melanomas (from TCGA) on the basis of images of H&E stained tumor
sections. ROC curves were obtained through: (left) predictingAPCloss-of-function
genotype (a Wnt pathway gene) and setting true positive labels to be either Wnt
pathway mutants or a Wnt-associated expression program; (middle) predicting
TP53loss-of-function genotype and setting true positive labels to beTP53mutants
orTP53-associated expression programs; or (right) predictingPTENloss-of-
function genotype and setting true positive labels to bePTENmutants (no
PTEN-associated expression program available).
RESEARCH | RESEARCH ARTICLE