Discovering and Interpreting Transcriptomic Drivers of Imaging Traits Using Neural Networks

Discovering and interpreting transcriptomic drivers of imaging traits using neural networks

Nova F. Smedley1,2,3, Suzie El-Saden1,2, and William Hsu1,2,3,4

1. Medical & Imaging Informatics, University of California Los Angeles, USA 2. Department of Radiological Sciences, University of California Los Angeles, USA 3. Department of Bioengineering, University of California Los Angeles, USA 4. Bioinformatics IDP,University of California Los Angeles, USA

Abstract Motivation: Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these “radiogenomic” studies often use linear, shal- low models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These current studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical traits, such as prognosis. Results: We present an approach based on neural networks that takes high-dimensional gene expressions as input and performs nonlinear mapping to an imaging trait. To inter- pret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outper- form comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that imaging traits had specific transcription patterns, e.g., edema and genes related to cellular invasion, and 15 radiogenomic associations were predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic associations with clinical value. Availability and implementation: https://github.com/novasmedley/deepRa diogenomics. Contact: [email protected] arXiv:1912.05071v1 [q-bio.QM] 11 Dec 2019

1 Introduction

Radiogenomic mapping is the integration of traits observed on medical images and traits found

at the molecular level, such as gene expression proﬁling [1, 2]. As such, the study of radiogenomics plays a role in precision medicine, where associations can describe prognosis or ther-

apy response [3, 4, 5]. A common approach to radiogenomic mapping involves dimensionality

reduction and pairwise comparisons [2, 6, 7, 8, 9, 10, 11] or predictive models, such as decision

1 trees [1, 12, 13, 14, 15] or linear regression [16, 17, 18, 19, 11]. Markedly, these approaches often require feature selection; assume linearity; and/or depend on pairwise associations, lim- iting their capacity to represent complex biological relationships.

Neural networks, with their ability to automatically learn nonlinear, hierarchical represen- tations of large input spaces [20, 21], are alternate approaches for radiogenomics [5, 22, 23,

24, 25]. However, current applications of neural networks have focused on diagnostic images as inputs to predict a single gene status and have excluded gene interactions [5, 22, 24]. To the best of our knowledge, no prior studies have interpreted the neural networks to ascertain what radiogenomic associations are learned. Towards understanding the biological basis of imaging traits, we thus present an approach using the representational power of neural networks to model tumor transcriptomes and nonlinearly map genes to tumor imaging traits. No a priori selection is used on the transcriptome.

Importantly, we provide approaches for understanding radiogenomic neural networks based on visualization techniques, such as input masking [26] and saliency maps [27]. Although neural networks are considered “black boxes”, these methods probe the trained neural networks to understand relationships between gene expressions and imaging traits. The methods can identify cohort-level imaging-transcriptomic associations, which we refer to as radiogenomic associations, and patient-level radiogenomic associations, which we refer to as radiogenomic traits. We validated the associations and traits generated by a network in a classiﬁcation task with known relationships, i.e., gene expressions (model input) and molecular subtypes (model output) [28]. As a use case, we study radiogenomic associations related to human-understandable imaging traits in magnetic resonance imaging (MRI) scans from patients with glioblastoma (GBM).

GBM is a grade IV malignant brain tumor with poor prognosis, and for which imaging is heavily used in diagnosis, prognosis, and treatment assessment. MRI traits like tumor enhancement, non-contrast enhancement, edema, and necrosis in Fig. 1, describe some of the visual, phenotypic variations between patients as they are diagnosed and treated. We present here extracted associations found by the radiogenomic neural networks using our approach, compare against previous work to show both new and consistent ﬁndings, and establish the radiogenomic associations’ clinical value in estimating patient survival over clinical or imaging traits alone.

2 Figure 1: Examples of phenotypic differences observed in glioblastoma patients. Shown are single, axial images of pre-operative MRI scans from the TCGA-GBM cohort. Four MRI se- quences were used to annotate imaging traits: T1-weighted (T1W), T1W with gadolinium contrast (T1W+Gd), and T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR) images. MRI traits included enhancing (enhan.), non-contrast enhancing tumor (nCET), necrosis (necro.), edema, infiltrative (infil.), and focal, where class labels were indicated by black (proportions < 1/3, expansive, focal) or gray (proportions 1/3, infiltrative, non-focal) blocks. ≥ T1W T1W + Gd T2W FLAIR enhan. nCET necro. edema infil. focal TCGA-02-0060 TCGA-06-5412 TCGA-76-6657 TCGA-14-1794

2 Materials and methods

2.1 Gene expression

Transcriptomes were available for 528 GBM patients as part of The Cancer Genome Atlas

(TCGA), see Supp. Table S1. Samples were primary tumors, untreated, and had 80% cancer ≥ and 50% necrotic cells [29]. Samples were analyzed by the Broad Institute on Affymetrix ≤ arrays, quantile normalized, and background corrected. Level 3 data were downloaded from the Genomic Data Commons at https://gdc.cancer.gov/. Each proﬁle had 12,042 genes.

2.2 Magnetic resonance imaging

Medical images for 262 GBM patients were downloaded from The Cancer Imaging Archive

(TCIA) [30]. Patients were matched based on barcodes shared by TCGA and TCIA. A board-

3 Figure 2: An overview of the study’s approaches to radiogenomic neural network (a) training and (b) interpretation, i.e., gene masking and gene saliency to extract radiogenomic associations and radiogenomic traits. a a GBM no pre- 10-fold transcriptomic cross-validation select best re-train fully trained transcriptomes op MRI n=353 split autoencoder mean R2 model model autoencoder n=528 apply architecture Legend: neural network pre-op MRI input random forest cross-validation select best re-train fully trained annotate 10-fold logistic regression mean AUC model model radiogenomic output n=175 an MRI trait split model gradient boosted trees autoencoder support vector machine modeling radiogenomic b gene GSEA enrichment modeling select a repeat for each patient set’s score between a gene set AUC, AP select a gene set and radiogenomic gene masking fully trained a mask gene MRI trait cohort gene set single genes’ associations select a expression radiogenomic gene’s classification specific to patient model prediction predictions gene profile AUC, AP rank scores in cohort gene set masking repeat for each gene specific to single gene masking take gene expression fully trained class gene profile with rank gene saliency profile, MRI trait radiogenomic model saliency sailency values enrichment select a score between a radiogenomic gene set and a traits gene set patient’s gene saliency values GSEA

certiﬁed neuroradiologist, Dr. El-Saden (26 years of experience), evaluated images using the

Osirix medical image viewer. An electronic form was used to record MRI traits according to

the Visually Accessible Rembrandt Images (VASARI) feature guide [31]. 175 patients had pre- operative (pre-op) MRIs and transcriptomes, see Supp. Table S1. Six MRI traits were annotated

from the pre-op studies. Traits were modeled as binary labels given the small sample sizes.

Supp. Table S2 describes all labels and their percentages in the cohort.

2.3 Radiogenomic modeling

To map relationships between gene expression proﬁles and MRI traits, feed-forward neural net-

works [21] were used, see Fig. 3a–b. Each MRI trait was a binary classiﬁcation task, where the positive class was the least frequent label (Supp. Table S2). Models were provided all 12,042

gene expressions as input vectors to classify each imaging trait, resulting in one model per trait.

During training, early stopping with a patience of 200 epochs was used while monitoring the

receiver operating characteristic curve, and the area under the curve (AUC) calculated. To help

learning in the radiogenomic model, an autoencoder was used since many more patients had

transcriptomic data. The radiogenomic neural networks were pretrained using weights trans-

ferred from a deep transcriptomic autoencoder trained on a separate subset of 353 patients.

The transcriptome dataset consisted of TCGA-GBM patients with transcriptomes, but no pre-op

MRIs and thus excluded the radiogenomic dataset.

4 Figure 3: The radiogenomic neural network’s (a) architecture, (b) transfer learning with a deep transcriptomic autoencoder, and interpretation methods through (c) gene masking and (d) gene saliency. Pretrained weights learned in the autoencoder were transferred to a radiogenomic model, where weights were frozen (non-trainable, long red arrows) and/or ﬁne-tuned (trainable, dashed red arrow) during radiogenomic training.

a halved c push through model classify a

gene masked record expression MRI trait gene MRI trait MRI trait proﬁle expression prediction proﬁle

= expression value (gene in gene set) encode decode b = zero value (gene not in gene set) = MRI trait prediction based on gene set transcriptomic autoencoder: class saliency: change in inputs that d increase the positive class probability

transfer weights + freeze gene record saliency MRI trait gene proﬁle saliency ﬁne-tune values radiogenomic model: = saliency value = affected MRI trait prediction

The transcriptomic autoencoder takes a gene expression profile as input, compresses the information through three encoding layers, and then decodes the information to reconstruct the transcriptome. Early stopping was used in training and monitored the mean coefficient of determination (R2) of each gene. The trained autoencoder weights, along with the gene pre-processing parameters, were then used as non-random weight initialization (i.e., weights can be fine-tuned during training) and/or frozen weights (i.e., weights cannot be fine-tuned during training) in the radiogenomic models.

Performance was estimated with 10-fold cross-validation with sample weighting and strat- iﬁed fold splitting. Each gene expression was mean subtracted and divided by its standard deviation, a process performed at each fold split. Hyperparameters were optimized via grid search. An illustration of the overall methods for radiogenomic model training is shown in Fig.

2a. The autoencoder achieved a mean validation of 0.45 R2 in cross-validation and 0.61 R2 in retraining, see Supp. Fig. S2. Radiogenomic models were then constructed to have the same encoding architecture, activation function, and optimizer as the best performing autoencoder.

Neural networks were trained on NVIDIA Tesla K80 and V100 GPUs through Amazon Web

Services using Python 3.6, Keras 2.2.4 [32], and TensorFlow 1.12.0 on a Ubuntu

16.04 machine. Other classiﬁers were implemented via XgBoost 0.80 [33] and sklearn

5 0.20.0 [34]. For more modeling details, see Supp. Table S3.

2.4 Bootstrapping

The best performing models of each model type (see Supp. Tables S3) were evaluated on bootstrapped datasets to measure classiﬁcation performance variability within and between model types. For each bootstrap, a radiogenomic dataset was split for 10-fold cross-validation using a different seed. In each split, training and validation samples were separately resampled with replacement to obtain bootstrapped sets. Sets were resampled if not all classes were observed. Each model was trained and compared on the same bootstrapped dataset using the same procedure in the aforementioned methods. This process was repeated 100 times for each

MRI trait.

2.5 Molecular subtype modeling

Molecular subtypes and their gene sets were downloaded from [28], which contained 840 genes to describe four GBM subtypes, i.e., classical, mesenchymal, proneural, and neural. Of the 528 patients, 171 had subtype labels and Affymetrix profiles, see Supp. Tables S1 and S2. A neural network was trained to predict subtypes using gene expression profiles. The four subtypes were modeled as multi-class classification task via one-hot-encoded labels. Gene expressions pre-processing and model training were performed in the same manner as the radiogenomic models, mainly, 10-fold cross-validation and hyperparameter grid search, see Supp. Tables S4.

2.6 Gene masking

Masking is a sensitivity analysis where the actual value of one or more components of the input are kept while all others are replaced with zeros. The goal is to determine the impact that the kept input components have on the end classification; this procedure was previously described in [26]. Here, we define “gene masking” to extract radiogenomic associations from a neural network, see Fig. 3c. For each individual, the gene expression values for a particular gene set were kept while all other expressions were replaced with zeros. The masked profiles were pushed through a fully trained neural network and the output, i.e., a class probability based on using genes from the gene set, was recorded. After repeating this process for each patient, classification performance was calculated. Each gene set was evaluated by AUC and average

6 precision (AP) to measure the strength of a radiogenomic association. As such, gene masking reported radiogenomic associations that were generally predictive of an MRI trait in the entire cohort.

In single gene masking, each gene was additionally used in gene set enrichment analysis

(GSEA) [35] (ranked by AP or AUC). We also used gene set masking, where predeﬁned gene sets smaller than 500 genes (see GSEA methods) from the Broad Institute’s molecular signatures database (MSigDB, v6.2) [36], molecular subtypes [28], and brain cell types and phenotypes

[37, 38, 39] taken from [40] were used. MSigDB was also queried for gene sets that include the 22 genes characterized as potential contributors of GBM tumorigenesis [29, 41], see Supp. information. The top performing genes or gene sets for each MRI trait were visualized together by clustering classiﬁcation scores using pheatmap in R.

2.7 Gene saliency

Class saliency is a visualization technique used to compute the gradient an output class prediction with respect to an input via back-propagation [27, 42]. Thus, class saliency identifies the relevant input components whose values would affect the positive class probability in a neural network. Here, we define “gene saliency” as the genes whose change in expression would increase the model’s belief of the positive class label, see Fig. 3d. In each model, salient genes are derived for each patient, ranked, and used in GSEA to determine if a gene set is relevant to predicting his/her MRI trait. Subsequently, positive enrichment between a single patient’s salient genes and a gene set is defined as a “radiogenomic trait.” For example, the edema model was probed to identify a single patient’s salient genes. The most salient genes were the genes that increased the probability of edema being 1/3. If GSEA found the salient genes were en- ≥ riched by a gene set, then the prediction of the patient’s edema was related to the gene set, i.e., the patient has the radiogenomic trait between the gene set and 1/3 edema. Saliency was ≥ implemented using keras-vis 0.4.1 [42]. The input range was determined by the gene expression range in the dataset, and guided was used as the backprop_modifier; other parameters were set to default. In the subtype neural network, gene saliency was repeated for each class as one-versus-others. Fig. 2b depicts the overall process for gene masking compared to gene saliency.

7 2.8 Gene set enrichment analysis

Pre-ranked GSEA [35] was implemented using fgsea [43]. Gene sets were parameterized by the recommended maximum size of 500 genes in a gene set, a minimum size of 15, and 10,000 permutations. Genes were ranked via single gene masking classification scores or gene saliency values for a patient. Enrichments were significant at an adjusted p-value < 0.05 [43]. Corre- lation between a gene expression and an imaging trait was used for comparison. Clustering of enrichment scores was performed as previously defined.

2.9 Survival

Clinical data from TCGA was used to define patient outcomes. Patient covariates included gender, race (binned as white and non-white), and age at initial diagnosis (binned above or below the median value in all 528 patients). Overall survival (OS) and time-to-death events were in the patient file. Progression-free survival (PFS) outcomes were defined by the followup

ﬁle, where all event types with days-to-event data were considered, but only the earliest event was used (see also Supp. information).

Cox proportional hazards models were used to estimate univariate and adjusted hazard ratios (HR). Radiogenomic traits enriched in at least ﬁve patients were kept. Adjusted HRs were estimated by a Cox model with backward feature selection and forced to keep all three patient covariates, while free to choose any MRI or radiogenomic trait.

Survival analyses were done in R using the survival and survminer. Kaplan-Meier estimates were obtained with survfit, ggsurvplot, and survdiff. Patients with missing information were removed and the log-rank test was used to test for signiﬁcant differences between groups. Cox models were obtained with coxph and feature selection performed with step.

3 Results

3.1 Neural networks achieve best performance in classifying MRI traits

Neural networks were better at estimating MRI traits than all other classifiers, see Fig. 4a. In predicting proportions of nCET, necrosis, and edema, the first hidden layer used frozen pretrained weights and only the last two hidden layers were used for fine-tuning, see Supp. Table

8 Figure 4: Radiogenomic models performances. (a) Observed 10-fold cross-validation performances. (b) Performance differences between a neural network and another model in 100 bootstrapped datasets. Notation: neural network (nn), gradient boosted trees (gbt), random forest (rf), support vector machines (svm), logistic regression (logit).

1.0 a 0.9

model 0.8 nn mean gbt AUC rf 0.7 svm logit 0.6

0.5 enhancing nCET necrosis edema infiltrative focal

enhancing nCET necrosis edema infiltrative● focal 0.6 b ● 0.5 ● ● ● ● ● ● comparison ● ● ● ● ● ● 0.4 ● nn − gbt

● ● nn − rf delta 0.3 nn − svm mean nn − logit AUC 0.2 confidence interval ● ● ● ● ● ● ● 0.1 ● 95% CI ● ● 0.0

1. Bootstrapping showed neural networks had higher performances, where 95% conﬁdence intervals (CI) in Fig. 4b indicate neural networks outperformed other models by more than

0.10 AUC. Supp. Table S5 and Figs S3–S5 have further modeling results.

3.2 Neural networks correctly learned known associations between gene expressions and molecular subtypes

To verify the relationships learned in the neural network’s layers, a molecular subtype model was trained. The model achieved a micro-averaged AUC of 0.994 in 171 patients, see Supp.

Table S6. Gene masking with subtypes gene sets showed the neural network was able to predict subtype classes with high certainty, see Fig. 5. For example, when the model was given the expressions of the 216 mesenchymal genes, subtype probabilities approached 1 or 0 and often corresponded to the correct subtype (0.90 AUC, 0.89 AP). Performance improved when the model was given all 840 subtype genes (0.99 AUC, 0.98 AP). Conversely, given the expressions of 200 random, non-subtype genes, the model was less certain (probabilities away from 1 or

0) and a fully trained performance of 1.0 AP dropped to 0.68 AP,see Supp. Table S7.

The majority of the top 20 predictive genes in each subtype class belonged to the original subtype class deﬁnition, see Fig. 6a. For example, 18 of the top 20 genes for predicting the

9 Table 1: Neural network hyperparameters. Layers refers to the depth of hidden layers in the radiogenomic model that used pretrained weights from the autoencoder (AE), e.g., two AE layers indicate the ﬁrst two layers used pretrained weights. Retrain refers to models trained on the full dataset.

CV means retrain label R2 eph R2 eph architecture opt act drop transcriptome 0.45 467 0.61 486 4000, 2000, 1000 Adadelta tanh 0.0 layers label AUC eph AUC eph architecture opt act drop AE frozen enhancing 0.72 38 1.00 14 4000, 2000, 1000 Adadelta tanh 0.6 3 0 nCET 0.83 38 1.00 11 4000, 2000, 1000 Adadelta tanh 0.0 1 1 necrosis 0.75 44 1.00 11 4000, 2000, 1000 Adadelta tanh 0.0 1 1 edema 0.78 109 1.00 16 4000, 2000, 1000 Adadelta tanh 0.0 1 1 inﬁltrative 0.78 70 1.00 12 4000, 2000, 1000 Adadelta tanh 0.0 2 1 focal 0.85 44 1.00 12 4000, 2000, 1000 Adadelta tanh 0.6 3 0 subtype 0.994 14 0.998 66 3000, 1500, 750 Nadam sigmoid 0.4 - - eph (epoch), opt (optimizer), act (activation), drop (dropout). proneural subtype were a part of the proneural gene set and each had least 0.80 AP and a

0.80 AUC. Of the top 500 genes ranked by AP, 270 genes (54%) were subtype genes; this represented 32% of all subtype genes, see Fig. 6b. As expected, subtype genes were predictive of more than one subtype. Similarly, GSEA showed the most predictive single genes for each subtype prediction were signiﬁcantly (adjusted p-value < 0.05) and positively enriched by the corresponding subtype gene set, see Fig. 6c. This observation was corroborated in GSEA using ranked genes based on the correlation, see Fig. 6d.

3.3 Genes driving the prediction of MRI traits

3.3.1 Enhancing tumor

Tumor enhancement was measured on T1W+Gd images. Low grade or well-differentiated brain tumors tend to generate blood vessels with intact blood-brain barriers (BBB) and do not enhance. Poorly differentiated or more aggressive tumors, like GBM, generate leaky blood vessels without an intact BBB and enhance on T1W+Gd images. Enhancement was found to be associated with growth, immune responses, hormones, the extracellular matrix (ECM), vasculature, and kinase activity in gene masking, see Table 2. The

ECM association included gene expressions of ECM-related proteins [44], see Supp. Fig. S18. Of the MSigDB hallmark gene sets, apical junction (cellular components, adherens and tight junctions), IL2/STAT5 signaling immune response activation), complement system, early and late responses to estrogen (associated with ESR1 expression), and heme metabolism (erythroid

10 Figure 5: Gene masking in the fully trained subtype neural network. The model’s (a) estimated subtype probabilities, where each row was a patient and grouped by their true subtype; and (b) classiﬁcation performance measured by AP in gene set masking, where each row was a gene set and each column was the subtype prediction. The random gene set excluded any gene included in a subtype set. For visualization purposes, rows were sorted by the model’s estimated mesenchymal probability. Notation: classical (CL), mesenchymal (MES), neural (NL), proneural (PN), coverage (percent of gene set that exist in gene expression proﬁles). For more gene masking, see Supp. Figs. S6–S8.

random 200 genes all subtype genes MES genes a CL

MES

probability 1.0

0.8 truth 0.6 NL 0.4

0.2

0.0

CL MES NL PN CL MES NL PN CL MES NL PN model prediction average precision b 1 NL gene set verhaak all 0.8 random MES num. genes CL 0.6 (50,100] PN (100,150] random 100 0.4 (150,200] (200,250] random 200 (800,850] gene set gene num. genes % coverage Neural Classical Mesenchymal Proneural 0.2 % coverage (90,100] 0

differentiation, STAT5 activation) [45] were most predictive of enhancement, see Fig. 7a. Gene Ontology (GO) gene sets related to GBM-abnormalities support the association of growth, immune system, and hormones with enhancement.

In single gene masking, enhancement was best predicted by SNTB1 (0.67 AUC, 0.58 AP) and

B4GALT6 (0.64 AUC, 0.60 AP), see Supp. Fig. S10. SNTB1, a cytoskeletal protein, was down- regulated in a GBM cells study [46] and a potential binder to PTPRZ, a protein contributing to glioma cell growth [47].

11 Figure 6: Single gene masking in the subytpe model: (a) the top 20 genes used to predict each subtype; (b) the percent of subtype genes covered in the top N genes; (c) GSEA with genes ranked by average precision, where positive enrichment indicated the subtype gene set was correlated with high average precision and vice versa; and (d) GSEA with genes ranked by correlation with label, where positive enrichment indicated the subtype gene set was correlated with subtype and vice versa.

In previous GBM radiogenomic studies, enhancement was associated with hypoxia, ECM, angiogenesis in 22 patients [2]; Biocarta pathways and genes, C1orf172, CAMSAP2, KCNK3, and LTBP1 in 23 patients [10]; and EGFR copy number ampliﬁcation in 75 patients [7]. Gene sets involving the ECM, EGFR, C1orf172, KCNK3, and LTBP1 were conﬁrmed to have performances greater than 0.70 in both AUC and AP,see Supp. Fig. S12a.

12 Figure 7: Gene masking of the radiogenomic models with the MSigDB hallmark gene sets [45].(a) Model performance in gene set masking. Shown are the top ﬁve gene sets ranked by average precision in each MRI trait, see also Supp. Fig. S9. (b) Enrichment among genes ranked by average precision in single gene masking. Positive enrichment indicated gene sets were predictive of an MRI trait and negative enrichment indicated the opposite. Shown are hallmarks with at least one signiﬁcant enrichment.

average precision AUC a 1 protein secretion protein secretion gene set 0.9 g2m checkpoint g2m checkpoint hallmark 0.8 dna repair dna repair num. genes mitotic spindle mitotic spindle 0.7 (0,100] heme metabolism heme metabolism 0.6 (100,200] 0.5 p53 pathway p53 pathway % coverage xenobiotic metabolism xenobiotic metabolism 0.4 (80,90] mtorc1 signaling mtorc1 signaling 0.3 (90,100] estrogen response early estrogen response early 0.2 estrogen response late estrogen response late 0.1 complement complement 0 il2 stat5 signaling il2 stat5 signaling il6 jak stat3 signaling il6 jak stat3 signaling apoptosis apoptosis myc targets v1 myc targets v1 uv response dn uv response dn fatty acid metabolism fatty acid metabolism kras signaling dn kras signaling dn adipogenesis adipogenesis hypoxia hypoxia epithelial mesenchymal transition epithelial mesenchymal transition apical junction apical junction myogenesis myogenesis gene set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative

normalized enrichment score adjusted p−value b 4 p < 0.05 e2f targets gene set e2f targets hallmark g2m checkpoint g2m checkpoint % coverage myc targets v2 2 myc targets v2 (80,90] hypoxia (90,100] hypoxia tnfa signaling via nfkb tnfa signaling via nfkb 0 num. genes estrogen response early (0,100] estrogen response early androgen response (100,200] androgen response cholesterol homeostasis −2 cholesterol homeostasis angiogenesis angiogenesis hedgehog signaling hedgehog signaling −4 p >= 0.05 xenobiotic metabolism xenobiotic metabolism epithelial mesenchymal transition epithelial mesenchymal transition uv response dn uv response dn oxidative phosphorylation oxidative phosphorylation fatty acid metabolism fatty acid metabolism mitotic spindle mitotic spindle gene set gene % coverage num. genes nCET focal edema necrosis enhancing infiltrative nCET focal edema necrosis enhancing infiltrative

3.3.2 Edema

Tumor edema was identiﬁed as abnormal hyperintensity on FLAIR or T2W images. Edema often co-occurs with enhancement, implying a more aggressive tumor and does not typically occur in low grade brain tumors. Edema also results from leaky capillaries and usually surrounds the tumor, spreading within the white matter. Edema suggests an inﬂammatory and/or immune response to a malignant tumor, which is essentially a foreign body when highly dedifferentiated.

Edema was associated with epithelial mesenchymal transition (EMT, metastasis and invasion), cell differentiation, and growth, see Table 2. p53 pathway (cell cycle, death), myogenesis, apical junction, heme metabolism, and glycolysis (cell metabolism) were the top hallmark gene sets, see Fig. 7a. GO terms relating to cell differentiation, death and adhesion with 0.80 ≥

13 Table 2: Summary of transcriptomic drivers of MRI traits in GBM patients. Shown are the top ﬁve hallmark gene sets ranked by AUC or AP and compared against gene sets related to prior GBM work, see Supp. Figs S11 (gene abnormalities [29, 41]) and S12–S15 (radiogenomics). Note: common themes can comprise of different gene sets.

transcriptomic drivers MRI trait theme gene set (collection*, query†) AUC AP see also

enhancing growth/death growth (GO, PTEN) 0.86 0.84 sensory organ development (GO, EGFR, KCNK3) 0.85 0.84 [10, 7] immune system IL2/STAT5 signaling (H) 0.77 0.76 complement system (H) 0.79 0.75 activation of immune response (GO, PTEN) 0.90 0.89 leukocyte & lymphocyte activation (GO, PIK3R1) 0.86, 0.85 0.85, 0.83 immune effector process (GO, PIK3CA) 0.87 0.84 hormones early & late responses to estrogen (H) 0.79, 0.78 0.73, 0.73 response to steroid hormone (GO, RBI) 0.88 0.88 regulation of hormone levels (GO, PARK2) 0.87 0.84 ECM related to ECM proteins (C, ECM) 0.77–0.84 0.73–0.76 [2] apical junction (H) 0.80 0.75 vasculature heme metabolism (H) 0.77 0.65 vasculature & heart development (GO, LTBP1) 0.81, 0.78 0.80, 0.77 [10] kinases activity multiple (GO, EGFR, LTBP1, KCNK3) all 0.87 all 0.85 [10, 7] edema EMT EMT (H) 0.80 0.80 positive regulation of locomotion (GO, EGFR, POSTN) 0.80 0.81 [6] taxis (GO, CXCL12, KIF5C) 0.80 0.80 [6] apical junction (H) 0.77 0.74 related to cell adhesion (GO, CDKN2A, EGFR, CTNNA2) 0.75–0.83 0.78–0.82 [6] growth/death p53 pathway (H) 0.77 0.77 autophagy (GO, CDKN2A) 0.75 076 myogenesis (H) 0.75 076 urogenital system development (GO, PTEN) 0.81 0.81 muscle structure development (GO, COL6A3) 0.80 0.80 [6] response to growth factor (GO, EGFR, POSTN) 0.76 0.81 [6] vasculature heme metabolism (H) 0.77 0.73 differentiation central nervous system neuron differentiation (GO, PTEN) 0.79 0.81 cell differentiation (GO, MET) 0.79 0.81 stem cell differentiation (GO, CDK6) 0.80 0.80 immune system regulation of cell activation (GO, CDKN2A) 0.83 0.82 neg. regulation of immune system (GO, CDKN2A, CXCL12) 0.82 0.81 [6] immune effector process (GO, PIK3CA) 0.80 0.81 other glycolysis (H) 0.76 0.70 nCET cell cycle mitotic spindle (H) 0.78 0.70 DNA repair (H) 0.77 0.66 G2M checkpoint (H) 0.72 0.63 regulation of mitotic cell cycle (GO, TP53) 0.78 0.71 growth/death p53 pathway (H) 0.76 0.65 urogenital & vasculature development (GO, PTEN) 0.81, 0.80 0.76, 0.71 neg. regulation of cell cycle (GO, TP53) 0.80 0.74 reproductive system development (GO, EGFR) 0.80 0.72 UV response UV response down (H) 0.78 0.59 response to radiation (GO, TP53) 0.83 0.73 other glycerphospholip metabolism process (GO, EGFR) 0.75 0.74 small molecule catabolic process (GO, PTEN) 0.78 0.73

* an MSigDB collection, where H=hallmarks, GO=Gene Ontology, and C=Canonical. † Gene sets queried from MSigDB using gene names or functions reported by previous work as keyword(s). neg.=negative.

AP,see Supp. Fig. S11b. Similar to the enhancement model, vasculature, immune system and

EGFR-related processes (albeit through different GO terms) were apart of the most predictive gene sets.

Growth and metastasis was also found to be predictive of edema in single gene masking.

RAI2, ANXA2, POSTN genes, all related to cell growth, were the top three single most predictive

14 Table 3: Summary of transcriptomic drivers of MRI traits in GBM patients, continued.

transcriptomic drivers MRI trait theme gene set (collection*, query†) AUC AP see also necrosis vasculature heme metabolism (H) 0.72 0.61 growth/death apoptosis (H) 0.71 0.58 apoptotic signaling pathway (GO, IL4, TP53) 0.72 0.63 [9] related to TP53 0.75–0.76 0.63–0.68 gland development (GO, EGFR) 0.76 0.65 immune system IL6/JAK/STAT3 signaling (H) 0.67 0.56 leukocyte cell cell adhesion (GO, IL4, ITGA5) 0.76 0.59 [9, 10] regulation of leukocyte proliferation (GO, CDKN2A) 0.77 0.61 [7] others xenobiotic metabolism (H) 0.76 0.65 related to PTEN 0.73–0.78 0.63–0.67 regulation of homeostatic process (GO, NF1) 0.78 0.65 glycolysis (H) 0.76 0.56 focal growth/death regulation of anatomical structure size (GO, PTEN) 0.96 0.88 response to growth factor (GO, EGFR) 0.92 0.83 transport secretion by cell (GO, NF1) 0.95 0.88 neg. regulation of transport (GO, PTEN) 0.96 0.87 regulation of cytoplasmic transport (GO, TP53) 0.95 0.85 monovalent inorganic cation transport (GO, PARK2) 0.95 0.81 response to steroid hormone, lipid, & organic cyclic compound (GO, RB1) 0.94–0.96 0.81–0.87 vasculature vasculature development (GO, PTEN) 0.93 0.84 muscle & circulatory system process (GO, PIK3CA) 0.94, 0.94 0.82, 0.83 oxygen hypoxia (H) 0.85 0.61 others genes down-regulated by KRAS (H) 0.88 0.64 protein heterodimerization activity (GO, TP53) 0.97 0.85 neg. regulation of intracellular signaling transduction (GO, PTEN) 0.96 0.84 synaptic singaling (GO, PTEN) 0.92 0.82 inﬁltrative oxygen reactive oxygen species pathway (H) 0.71 0.50 response to oxygen levels (GO, TP53) 0.67 0.60 transport neg. regulation of transport (GO, PTEN, NFKBIA) 0.74 0.64 [48] healing wound healing (GO, NF1) 0.80 0.64 hemostasis (GO, PIK3CA) 0.78 0.59 growth/death developmental growth (GO, PTEN) 0.74 0.62 spinal cord development (GO, NF1) 0.66 0.60 response to response to drug (GO, MDM2 , MYC) 0.75 0.60 response to inorganic substance (GO, PTEN) 0.73 0.61 [48] others DNA repair (H) 0.70 0.58 ligase activity (GO, MDM2) 0.75 0.64 ubiquitin like protein transferase activity (GO, MDM2) 0.70 0.59 ubiquitin like protein ligase binding (GO, NFKBIA) 0.67 0.57 [48] related to protein & transcription factor complex (GO, TP53) 0.74–0.75 0.60–0.63 WNT signaling pathway (GO, PTEN, MYC) 0.73 0.62 [48] * an MSigDB collection, where H=hallmarks, and GO=Gene Ontology. † Gene sets queried from MSigDB using gene names or functions reported by previous work as keyword(s). neg.=negative. gene with 0.68–0.70 AUC and 0.60–0.64 AP.The top three ranked by AP were MTSS1, LAMA5, and KLHDC3, with 0.65–0.66 AP and 0.63–0.67 AUC, see Supp. Fig. S10. Both MTSSI and

LAMA5 were associated with metastasis [49]. GSEA showed signiﬁcant enrichment in EMT, angiogenesis, androgen response (hormone), hedgehog signaling (including MTSSI), and xenobiotic metabolism (drug metabolism), see Fig.

7b. The appearance of drug metabolism could be due to the use of symptomatic relief drugs prior to surgery, such as corticosteroids for patients with neurologic symptoms caused by edema

[50, 51]. Previously, POSTN was associated with edema in 78 GBM patients; the authors suggested

15 POSTN was regulated by miR-219 and contributed to cell migration or invasion [6]. GO gene sets related to the study’s top ﬁve upregulated genes and microRNAs were found to be predictive of edema, see Supp. Figs. S13. Table 2 shows an overlap of gene set patterns between the study’s ﬁndings and gene masking of the edema model. In particular, gene sets associated

POSTN, cell taxis, and cell adhesion added to the association between edema and EMT.

3.3.3 Non-contrast enhancing tumor

Non-contrast enhancing tumor (nCET) was best identiﬁed on contrast-enhanced T1W and

FLAIR or T2W images. nCET is typically lower grade tumor (better cellular differentiation, more closely resembling normal brain tissue), generating vessels with an intact BBB, and ab- sent of contrast enhancement. While the abnormality on images is a mass-like neoplastic tissue, it is not rapidly dividing or aggressively dedifferentiating.

Cell cycle, growth, and radiation response were themes among the most predictive gene sets for nCET. Mitotic spindle, UV response down (genes down-regulated in response to ultraviolet radiation), DNA repair, and p53 pathway were predictive of nCET, see Fig. 7a. Of the gene sets related to GBM genomic alterations, the nCET model had a mix of ones found in the enhancing and edema model, see Fig. S11c, and supported transcription patterns found in hallmark gene sets in Table 2.

SGPL1 (0.73 AUC, 0.56 AP) and DDR1 (0.66, 0.61 AP) were the top performing genes in single gene masking of the nCET model, see Fig. S10, where hypoxia, TNFA signaling via NFKB, and oxidative phosphorylation were signiﬁcantly enriched, see Fig. 7b. The latter two gene sets were also identiﬁed in gene set masking, see Supp. Fig. S9.

3.3.4 Necrosis

Tumor necrosis was evaluated as the area of ﬂuid signal intensity on T1W+Gd images. As tumors proliferate, they create new blood supply (angiogenesis) and/or expands to recruit blood from adjacent tissue. Subsequently, necrosis occurs, typically within the central portions of an aggressive tumor as the outer rim of enhancing surviving cells can be observed on MR images.

Vasculature, apoptosis, immune system, and homeostasis was associated with necrosis, see

Table 3. Predictive GO terms for necrosis included several TP53 and PTEN related processes.

16 Gene masking found some gene sets related to IL4, WWTR1, RUNX3, ITGA5, and CDKN2A

(found in previous radiogenomic studies [9, 10, 7]) support the association between necrosis and apoptosis and the immune system. Earlier, drug metabolism was predictive of edema, but was also predictive of necrosis. Besides corticosteroids, antiepilepics can be prescribed patients who experience seizures from tumors [50]. In single gene masking, CACNB2 (0.67 AUC, 0.51 AP) and PACSIN3 (0.64 AUC, 0.51 AP) the most predictive genes for necrosis, see Supp. Fig. S10. Notably, the MYC targets hallmark was signiﬁcantly enriched, see Fig. 7b.

3.3.5 Focal vs. non-focal

Focal vs. non-focal traits were determined via T1W+Gd and FLAIR or T2W images. Focal tumors appear in one region. Non-focal tumors included those described as multifocal, multicentric, or with gliomatosis cerebri. A multifocal tumor is one with separate enhancing regions that appear connected on FLAIR/T2W images with contiguous hyperintensity spreading via white matter tracts. A multicentric GBM has multiple enhancing or non-enhancing tumors growing synchronously without contiguity on FLAIR/T2W. Gliomatosis cerebri is a rare, diffusely inﬁl- trating subtype and involve at least three cerebral lobes.

Focal traits were associated with growth, transport, vasculature, and hypoxia, see Table 3.

Several of these involved PTEN and RB1. Secretion by cell includes genes potentially overlapped by others, e.g., NF1, EGF, and VEGF.

In general, focal traits were better predicted by GO gene sets related to GBM genes (Supp

Fig. S11e) than hallmark gene sets (Fig. 7) or single genes (Supp. Fig. S10). These highly predictive GO gene sets ( 0.90 AUC and 0.80 AP) indicated broad tumor characteristics, e.g., ≥ ≥ proliferation (growth, response to growth factors, secretion of growth factors) resulting in the need for angiogenesis (vasculature development), were used by the focal model to determine focal vs. non-focal tumors.

3.3.6 Expansive vs. inﬁltrative

Expansive vs. inﬁltrative was measured as the ratio of T1/FLAIR abnormality on T1W and

FLAIR or T2W images. Expansive tumors have similar distribution on T1W as on FLAIR/T2W; the closer the two, the better deﬁned the tumor margins and the better for surgical resection.

17 Infiltrative tumors have FLAIR/T2W abnormality that is large compared to T1W abnormality, where the tumor is spreading through white matter tracts to cause large edema relative to the core tumor mass. Infiltrative traits indicate ill-defined tumor margins, less successful surgical debulking, and worse prognosis.

Inﬁltrative traits were best predicted by gene sets related to oxygen, transport, healing, and growth, see Table 3. Gene masking showed that GO gene sets were more predictive than hallmark gene sets (Fig. 7). Of the top GO gene sets, would healing and hemostasis were the most predictive and several included TP53, MDM2, and PTEN. Notably, MDM2 transcription is regulated by TP53.

Previous radiogenomic studies related to expansiveness or infiltrative traits found associations with MYC, NFKBIA, and immune cell gene modules [48]. The infiltrative model was masked with related gene sets and was able to predict infiltrative tumors with 0.50–0.70 AP, see Supp. Fig. S15.

In single gene masking, ZBTB48 (0.68 AUC, 0.53 AP) and PRTN3 (0.67 AUC, 0.57 AP) as the best single gene predictors in Supp. Fig. S10. For more gene masking, see Supp. Figs

S16–S21.

3.4 Radiogenomic traits: Patient-speciﬁc radiogenomic associations

Gene masking was used to identify cohort-level radiogenomic associations as genes were ranked by their overall classiﬁcation performance among all tumors. In contrast, gene saliency was measured for each patient and identiﬁed patient-level radiogenomic associations, termed ’radiogenomic traits.’ For salient genes in the subtype model, see Supp. Fig. S25.

Classical subtype genes were salient in predicting larger proportions of necrosis, where

77 patients were enriched with classical genes in the necrosis model, see Fig. 8. Similarly, neural and proneural gene sets were associated with greater edema and nCET proportions, respectively.

The nCET model showed more than 40 patients had salient genes enriched by oligodendrocytes, mature astrocytes, and hypoxia gene sets. Larger edema proportions were associated with neurons and replicating fetal neurons genes. The anti-cell cycle genes (negatively correlated with the cell cycles genes, some of which were a part of the hypoxia gene set in [37]) were associated with prediction of the non-focal class (35 patients enriched). Patients were not

18 Figure 8: Radiogenomic traits: results of gene saliency applied to the neural networks. Patients were considered enriched for a gene set at an adjusted p-value < 0.05. Gene sets with at least 10 enriched patients in a model were shown. All enriched patients were positively enriched. Positive enrichment indicated that the gene set was among the most salient genes for predicting a single patient’s imaging trait.

subtype 80 60 40 20 0 CL NL PN cell types or phenotypes 80 60 40 20 0 Darmanis Darmanis Patel Patel Zhang Zhang Zhang Zhang radiogenomic fetal oligo− anticell hypoxia mature microglia neuron oligo− model neurons dendrocytes cycle astrocytes macrophages dendrocytes # replicating nCET enriched necrosis patients hallmark edema focal 80 infiltrative 60 40 20 0 allograft e2f epithelial g2m glycolysis hypoxia tgf tnfa xenobiotic rejection targets mesenchymal check− beta signaling metabolism transition point signaling via nfkb chromosome 80 60 40 20 0 chr19p13 chr19q13 chr1p35 chr3p21 chr3p22 chr6q27 gene sets signiﬁcantly enriched with cell type or phenotype in the enhancing model, possibly reﬂecting more tumor heterogeneity in patients with more enhancing and aggressive tumor.

The hypoxia and nCET association (66 enriched patients) was consistent with the aforementioned anti-cell and hypoxia ﬁndings and with gene masking analysis, where vasculature development was predictive of nCET at the cohort-level. The association between hypoxia and greater proportions of nCET may be linked to lower-grade cells in the beginning stages of aggressive tumor growth and therefore responding to the beginning stages of hypoxic conditions and driving angiogenesis. Xenobiotic metabolism was enriched in 15 patients for predicting

19 larger nCET, suggesting an increase in tumor size will result in an increased dosage of drugs administered prior to surgical resection or biopsy [51]. Interestingly, the edema model showed only two hallmarks enriched by more than 10 patients. Although gene masking showed the edema model had high overall performance with the EMT gene set, other genes that are not associated with a predefined gene set may have been more influential in predicting each individual patient’s edema proportions. In fact, the EMT hallmark was more associated with the radiogenomic model’s belief of an infiltrative tumor in

27 patients. This subset of patients support the hypothesis that tumor cells with alterations in EMT-related genes are driving the observation of higher edema proportions than tumor cell proportions. Glycolysis and hypoxia hallmarks were also moderately (< 25 patients) associated with inﬁltrative tumors. TNFA signaling via NFKB was associated with larger proportions of necrosis in 34 patients.

Chromosomal aberrations have been reported in GBM [52, 28]. There were 86 and 82 patients who were enriched by the chr19p13 gene set in predicting their necrosis and inﬁltrative traits, respectively. Genes in chr19q13, chr1p35, and chr6q27 were also salient to inﬁltrative tumors. chr3p21 and chr3p22 gene sets were also salient for greater nCET proportions.

3.4.1 Radiogenomic traits with survival implications

Of the 175 patients with radiogenomic data, 127 had all six MRI traits labeled and outcomes data. Given that only a subset of these individuals had clinical, imaging, and transcriptomic data to perform a survival analysis, we tested whether this subset of patients had any differences in outcomes compared to the transcriptome cohort (n=528). There were no overall survival (OS) or progression-free-survival (PFS) differences between the transcriptome cohort, the subset of patients with MRI traits, and the subset of patients with all six MRI traits, see

Supp. Fig. S27. In building the multivariable Cox models, three clinical traits, six MRI traits, and 54 radiogenomic traits were considered, see Supp. Fig. S29.

Figure 9 show patients had significant differences when dichotomized by their radiogenomic traits. Patients with neural genes among the most salient genes for predicting larger nCET had better PFS compared to those who did not. Likewise, patients had significantly worse OS when chr3p21 genes were important in predicting nCET proportion was 1/3, and when chr19p13 ≥ or chr1p35 genes were important in predicting infiltration. In contrast, dichotomizing patients

20 Figure 9: Overall survival (OS) and progression-free survival (PFS) dichomotized by (a) imaging traits compared to (b) radiogenomic traits. Patients split based on the association between the nCET trait and neural (NL) subtype genes had a median PFS of 0.96 years vs. 0.52 years (161 day difference). The median OS was 1.19 years vs. 0.91 years (101 day difference) when split by the nCET and chr3p21 trait, 1.18 years vs. 1.14 years (15 day difference) split by the inﬁltrative and chr19p13 trait, and 1.19 years vs. 0.85 years (125 day difference) split by the inﬁltrative and chr1p35 trait.

a 1.00 ++ b 1.00 ++ ++ + nCET < 1/3 ++ + nCET + NL not enriched + ++ + + + + + nCET >= 1/3 ++ nCET + NL enriched 0.75 + 0.75 + + ++ ++++ ++ ++ PFS p = 0.77 PFS + ++ p = 0.022 0.50 + 0.50 probability +++ probability + + + + +++ 0.25 ++ 0.25 ++++ + + ++ + ++ 0.00 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 93 19 4 3 2 0 0 103 19 5 3 2 0 0

− 54 8 2 0 0 0 0 − 44 10 3 1 0 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years

1.00 1.00 + + nCET < 1/3 + nCET + chr3p21 not enriched + + + + nCET >= 1/3 + + nCET + chr3p21 enriched 0.75 + 0.75 + +++ ++++ ++ +++ OS +++ p = 0.62 OS + + p = 0.033 0.50 + 0.50 + probability probability +++

0.25 0.25 + ++ + +++ + + ++ 0.00 + 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 93 46 16 7 3 1 0 98 53 17 7 1 1 0

− 54 27 9 3 1 0 0 − 49 16 3 0 0 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years

1.00 1.00 + + expansive + + infiltrative + chr19p13 not enriched + + infiltrative + infiltrative + chr19p13 enriched + + 0.75 + 0.75 +++ ++ ++++ + ++ OS + p = 0.15 OS + p = 0.028 0.50 + 0.50 +++ probability + probability

0.25 + 0.25 + ++ ++ +++ +++ + ++ + ++ 0.00 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 96 46 22 9 2 1 0 70 36 13 9 4 1 0

− 54 28 5 3 2 0 0 − 80 37 13 2 0 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years

1.00 + + infiltrative + chr1p35 not enriched + + + infiltrative + chr1p35 enriched 0.75 + +++ ++ +++ OS ++ p = 0.0072 0.50 probability

0.25 + ++ +++ + ++ 0.00 0 1 2 3 4 5 6 years Number at risk 140 69 26 11 4 1 0

− 10 4 0 0 0 0 0 − 0 1 2 3 4 5 6 years

21 Table 4: Cox regression analysis of traits associated with overall survival and progression-free survival. Both OS and PFS multivariable models had p<0.001 in the likelihood ratio test.

univariate HR adjusted HR (95%CI) (95%CI) p-value Overall survival (n=127, deaths=107) clinical gender is male 0.95 (0.64–1.41) 0.80 (0.53–1.20) 0.280 race is white 1.69 (0.85–3.37) 2.30 (1.09–4.85) 0.029* diagnosis age is below median 0.82 (0.56–1.21) 0.79 (0.53–1.20) 0.273 radiogenomic infiltrative + chr1p35 2.06 (0.98–4.31) 2.06 (0.95–4.43) 0.066 † edema + endothelial 2.07 (0.76–5.67) 4.36 (1.47–12.89) 0.008* † necrosis + GBM core astrocytes 0.46 (0.14–1.49) 0.11 (0.03–0.45) 0.002* necrosis + epithelial mesenchymal transition 1.40 (0.80–2.43) 3.45 (1.75–6.82) <0.001* nCET + myogenesis 2.85 (0.89–9.07) 10.73 (2.48–46.51) 0.002* necrosis + MYC targets (v2) 0.76 (0.31–1.87) 0.31 (0.10–0.98) 0.045* infiltrative + mTORC1 signaling 1.87 (0.76–4.61) 2.38 (0.93–6.10) 0.071 Progression-free survival (n=127, progressions=88) clinical gender is male 1.03 ( 0.65–1.62) 0.80 (0.48–1.34) 0.394 race is white 1.28 (0.62–2.65) 1.96 (0.89–4.30) 0.094 diagnosis age is below median 0.98 ( 0.64–1.50) 1.25 (0.77–2.04) 0.373 imaging tumor was infiltrative 0.92 (0.58–1.45) 1.61 (0.96–2.71) 0.072 radiogenomic infiltrative + chr1p35 2.06 (0.98–4.31) 4.20 (1.89–9.33) <0.001* infiltrative + epithelial mesenchymal transition 2.57 (1.23–5.39) 2.24 (1.27–3.96) 0.006* necrosis + MYC targets (v2) 0.21 (0.03–1.49) 0.06 (0.01–0.47) 0.008* †† edema + fetal neurons replicating 1.48 (0.93–2.35) 2.50 (1.44–4.33) 0.001* infiltrative + TGF-β signaling 1.51 (0.80–2.86) 1.91 (0.94–3.92) 0.076 edema + chr18p11 2.22 (0.80–6.15) 5.79 (1.87–17.99) 0.002* edema + G2M checkpoint 0.75 (0.36–1.56) 0.46 (0.20–1.05) 0.066 nCET + chr22q13 0.35 (0.09–1.42) 0.36 (0.08–1.60) 0.180 infiltrative + chr6q27 1.33 (0.76–2.33) 1.79 (0.98–3.30) 0.060 necrosis + p53 pathway 1.06 (0.39–2.91) 2.44 (0.81–7.39) 0.114 † †† *p<0.05, gene set from [39], gene set [38] based solely on individual MRI traits had no OS or PFS differences, except in the counterintu- itive case of expansive vs. infiltrative, see Supp. Fig. S28. Infiltrative tumors had a univariate

HR of 0.92 when estimating PFS, see Table 4. However, after adjusting for patient covariates and radiogenomic traits, inﬁltrative tumors had an adjusted HR of 1.61 and correctly follows the intuition that inﬁltrative tumors would have a higher probability of progression than expansive tumors.

Males and non-white races had better OS and PFS, while patients diagnosed below the median age had better OS, but worse PFS. The final Cox model consisted of six significant traits, five radiogenomic traits and race when estimating OS, see Table 4. The final PFS model had five significant radiogenomic traits. In comparison, a Cox model with clinical and imaging traits had no significant factors in estimating OS or PFS. The survival analyses suggest that radiogenomic traits extracted from neural network models have prognostic value.

22 4 Discussion

We demonstrate how deep neural networks can be used to discover radiogenomic associations. First, we predict imaging traits using gene expression profiles, showing that our neural network-based approaches outperforms other classifiers. We also illustrate the benefit of transfer learning to train radiogenomic neural networks using a transcriptomic autoencoder modeled on a much larger cohort to address the impedance of relatively small radiogenomic datasets. Second, we present methods based on input masking and class saliency that facili- tate interpretation of radiogenomic associations, providing a way to understand the results of an otherwise “black box" method, which is the main criticism against neural networks. Third, using our network analysis techniques, we identify pertinent gene expressions that may act as transcriptomic drivers for each imaging trait. We put forth a set of potential imaging surrogates that provide a clearer biological basis of commonly assessed imaging phenotypes in GBM and relate them to trends in patients’ overall and progression-free survival.

Gene masking identiﬁes cohort-level radiogenomic associations, where the strength of association was measured by the model’s classiﬁcation when only a subset or one gene was used.

Radiogenomic associations have common themes related to major GBM candidate driver genes and terms, e.g., cell growth and vasculature. However, each MRI trait also show speciﬁcity towards components of these general themes, e.g., different functionalities of EGFR or cell death by autophagy in the edema model compared to apoptosis in the necrosis model. We identify unique associations between imaging traits and different themes: edema is associated with cell invasion and differentiation; enhancement is associated with immune system processes and hormones; nCET is associated with cell cycle and UV response; necrosis is associated with apoptosis; and focal is associated with cell transport and response to certain compounds.

Prior radiogenomic studies have mainly reported cohort-level associations. In reality, multiple gene expression proﬁles, when inﬂuenced by different environmental factors, may lead to the same observed imaging trait. Towards this end, gene saliency was used to identify patient- level radiogenomic traits.

With gene saliency, each patient has his/her own list of relevant genes for each imaging trait; it is then determined if the patient’s salient genes are signiﬁcantly associated with a gene set. We describe subsets of patients with common radiogenomic associations that are not apparent in gene masking, such as the association between inﬁltrative traits and epithelial-

23 mesenchymal transition genes or larger nCET proportions and drug metabolism. Some of these radiogenomic traits are signiﬁcant factors in predicting patient survival.

Furthermore, we validate our modeling approach by training a neural network to predict molecular subtypes. We report an experiment that evaluates model’s ability to learn mean- ingful relationships. Not only does the subtype model achieve near-perfect classification, the model is able to select genes relevant to each subtype among 12,042 genes. In the radiogenomic models, we validate our radiogenomic associations with prior GBM studies in radiogenomics and genomics and found corresponding relationships. We also identify new findings that have not been widely reported in radiogenomics due to the ability of gene saliency to provide patient-specific radiogenomic traits and the inclusion of the entire gene profile in our models. These results support the neural network’s abilities to identify associations with exist- ing domain knowledge and to suggest potential starting points for further investigation.

We recognize the limits of radiogenomic analysis, particularly in terms of small sample size, limited tissue sampling of a heterogeneous tumor, and limited follow-up information. Sample size is an inherent challenge in radiogenomics. TCGA tends to have the most radiogenomic data, but lacks detailed clinical data. While larger cohorts do exist, tumors are across multiple grades [8] and do not use molecular profiling [5]. These limitations may be addressed as the cost of high-throughput platforms decreases and multiple tumor regions are sampled [40]. With 528 gene expression profiles and a radiogenomic subset of 175, we show that neural networks can model transcriptomic heterogeneity to reflect phenotypic differences in imaging.

The VASARI feature set is also limited in that it provides a gross categorization of imaging features and only one experienced reader’s annotations of the imaging data is used. A more comprehensive analysis that includes quantitative (radiomic) traits may be warranted. Finally, the radiogenomic associations are only hypothesized and not proven through experimentation, though we attempt to compare our discovered associations with those that have been previously reported in literature. To validate the identiﬁed associations, cell and animal studies would allow controlled experiments between genes and imaging phenotypes [53].

5 Conclusion

Using a neural network-based approach to radiogenomic mapping, we highlight the representational and discriminative capacity of neural networks to model the high-dimensional, non-

24 linear, and correlative nature of gene expressions to predict typical GBM imaging traits. We demonstrate the use of neural network interpretation techniques, e.g., input masking and class saliency to understand what the model has learned and to extract relevant radiogenomic associations. The learned radiogenomic associations may point to potential transcriptomic drivers of imaging traits and could further clarify the understanding of the relationship between two often disjoint datasets, gene expression proﬁling and medical imaging. As such, prognostica- tion and treatment options may be further individualized, where a targeted pathway could be considered in the selection of an appropriately tailored chemotherapeutic agent.

6 Acknowledgements

We are thankful for the feedback from the faculty and students of the Medical & Imaging In- formatics group, discussions about performance differences with biostatisticians, Drs. James Sayre and Audrey Winter, and correspondences with TCIA members to better understand the data.

7 Funding

This work was supported in part by the National Institutes of Health [F31CA221061 and T32EB016640 to N.F.S., R01CA157553 to N.F.S., W.H., S.E.S.]; the National Science Foun- dation [#1722516 to W.H.]; Amazon Web Services and UCLA Department of Computational Medicine partnership to W.H.; and the Integrated Diagnostics Program, jointly funded by the Department of Radiological Sciences and Pathology & Laboratory Medicine to N.F.S., W.H.. Conﬂict of Interest: none declared.

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28 Supplemental data

Dataset

Supplemental Materials contains the following data used in this study:

• gene_expression.txt - Affymetrix transcriptomes

• gene_expression_ids.txt - patients’ TCGA barcodes

• TCGA_clinical_data - TCGA-GBM clinical data

• TCGA_unified_CORE_ClaNC840.txt - molecular subtype and gene sets from Verhaak et al.

• vasari_annotations.csv - MRI traits

Generated data and models can be found in the paper’s source code on Github.

1 Table S1: Patient characteristics with transcriptomes. Transcriptomes were analyzed with Affymetrix HT Human Genome U133 Arrays by the Broad Institute. The quantile normalized and background corrected (GenePattern platform; Level 3) data were downloaded from the Genomic Data Commons using the legacy data portal at https://gdc.cancer.gov/ . The radiogenomic subset is based on patients with any pre-op MRI study. The Verhaak et al. study was based on uniﬁed gene expressions from multiple platforms. Here, we took gene expression proﬁles measured on the same an Affymetrix platform as the radiogenomic models to create the subtype subset.

modeling datasets total autoencoder radiogenomic subtype number of patients 528 353 175 171 diagnosis age (years) min. 10 10 14 17 mean 57.9 57.1 59.4 56.3 med. 59 58 60.5 57.5 max. 89 89 86 86 vital status deceased 388 250 138 146 alive 113 85 28 8 n/a 27 18 9 17 gender female 196 134 62 56 male 306 202 104 98 n/a 26 17 9 17 race white 443 302 141 135 african 29 17 12 6 asian 11 7 4 5 n/a 45 27 18 25 ethnicity hispanic 12 9 3 5 not hispanic 415 278 137 141 n/a 101 66 35 25 KPS min. 20 20 40 40 mean 77 76 78 82 med. 80 80 80 80 max. 100 100 100 100 diagnosis method biopsy 63 46 17 6 resection 435 288 147 147 other 2 1 1 0 n/a 28 18 10 18 molecular subtype classical 38 23 15 38 mesenchymal 54 26 28 54 neural 26 12 14 26 proneural 53 30 23 53 n/a 357 262 95 0 KPS (Karnofsky Performance Score), n/a (not available)

2 Table S2: Classiﬁcation labels in the radiogenomic and subtype datasets. Based on personal communication with TCIA, the most recent imaging studies without signs of surgery or biopsy were estimated to be images closely associated with time of tissue sampling, as pre-op imaging was necessary for surgical planning. (GBM patients usually undergo surgical resection to remove the bulk of the tumor.) Proportion labels were binarized using 1/3 as the threshold due to small numbers in other categories. Likewise, subcategories of multifocal≥ or multicentric (24 patients) and gliomatosis (2 patients) were combined into one class, non-focal.

n trait description values # (%) 175 surgical evidence of prior surgery or biopsy in the pre-op 175 (100%) earliest imaging study post-op 0 (0%)

166 f5 proportion of tumor estimated to be enhancing < 1/3 95 (57%) 1/3 71 (43%) ≥ 156 f6 proportion of tumor estimated to be < 1/3 99 (63%) non-contrast enhancing and not edema 1/3 57 (37%) ≥ 167 f7 proportion of tumor estimated to be necrosis < 1/3 116 (70%) 1/3 51 (30%) ≥ 161 f9 lesions outside of main tumor and its edema: a) none focal 135 (84%) b) spread via dissemination non-focal 26 (16%) or in majority of a hemisphere 159 f10 ratio of abnormality sizes in T1 and FLAIR: a) T1 FLAIR expansive 103 (65%) b) T1 ≈< FLAIR or T1 << FLAIR inﬁltrative 56 (35%)

162 f14 proportion of tumor estimated to be edema < 1/3 86 (53%) 1/3 76 (47%) ≥ 171 subtype molecular subtypes deﬁned by Verhaak et al. classical 38 (22%) mesenchymal 54 (32%) neural 26 (15%) proneural 53 (31%)

3 Figure S1: Label association testing with Fisher’s exact test (a) within labels and (b) with clinical traits in R using fisher.test. P-values were adjusted using Bonferroni correction in p.adjust. Multi-class labels do not have estimates, i.e, odds ratios. For binary labels, labels “< 1/3", “focal", and “expansive" were class 1 and “ 1/3", “non-focal", “infiltrative" were class 2. Rows are the explanatory variable. E.g., the≥ odds of nCET < 1/3 and an expansive tumor were 3.353 times higher than nCET > 1/3 and an expansive tumor. Estimates above 1 indicated positive correlations, and vice versa. The traits infiltrative was positively correlated with the traits focal and nCET proportions. nCET was negatively correlated with edema and necrosis. No other associations were significant. Diagnosis age was dichotomized based on the mean diagnosis age of 58, a value calculated from all patients whose diagnosis age was known. Patients with MRI traits had tissue samples obtained from eight different sites (site codes: 02, 06, 08, 14, 19, 27, and 76). Karnofksy performance scores included 40, 60, 80, and 100; the higher the better. a edema 0.48 edema

infiltrative 1 1 infiltrative 0.881

focal 0.527 4.489 focal 1 1 0.033

necrosis 0.645 0.855 0.828 necrosis 1 1 1 1

nCET 0.259 3.353 3.31 0.249 nCET 1 0.004 0.016 0.208 0.02 enhancing 1.213 0.897 0.569 0.434 0.546 enhancing 1 1 1 1 0.556 1 subtype edema infiltrative focal necrosis nCET subtype edema infiltrative focal necrosis nCET estimate adjusted p−value < 0.05 no yes 0 1 5 b subtype 1 1 1 1 subtype

necrosis 1 1 1 0.763 necrosis 0.858 0.99

nCET nCET 1 1 1 1 1.134 1.372 infiltrative 0.932 0.53 infiltrative 1 1 1 1 focal 0.686 0.813 focal 1 1 0.317 1 enhancing 0.775 0.728 enhancing 1 1 1 1 edema 1.656 1.48 edema 1 1 1 1 diagnosis gender Karnofsky race diagnosis gender Karnofsky race age performance age performance score score estimate adjusted p−value < 0.05 no 0 1 2

4 Models

Radiogenomic neural networks were fully connected, had a batch size of ten. Regularization with dropout was used, where the same dropout rate was applied across the input and all hidden layers. For binary classes, 0 and 1 labels, binary cross-entropy loss and sigmoid activation in the prediction layer was used. Since subtypes were multi-classes, the subtype neural networks instead used categorical cross-entropy loss and softmax activation in the prediction layer. Transcriptomic autoencoders considered five different architectures of three encoding layers: the first hidden layer was either 1000, 2000, 3000, or 4000, where subsequent encoding layers decreased by half each time. The autoencoder used mean absolute error as the loss function, a batch size of 50, a patience of 200 epochs while monitoring validation R2, and no dropout. All neural networks used batch normalization in each hidden layer and a maximum of 500 epochs was set. Due to the inherent class imbalance of imaging traits, sample weighting based on class size and stratified fold splitting were used.

Table S3: Radiogenomic models and hyperparameters. model type no. models hyperparameter values logistic regression 4000 penalty type L1, L2 C penalty log(3) - log(1) solver liblinear, newton-cg, lbfgs, sag, saga support vector machines 4000 kernel linear, poly, rbf, sigmoid C penalty log(-6)-log(1) random forest 1200 trees [50: 50: 2000] split criterion Gini, entropy

max. features G, pG, log2(G) max. depth None, [1-4] gradient boosted trees 760 trees [50: 50: 1000) max. depth [1-4] learning rate [0.01: 0.05: 0.50] neural network, 40 hidden layers 3 autoencoder hidden nodes [4000-250] architectures 4 optimizer Nadam, Adadelta activation sigmoid, tanh, relu dropout [0.0:0.2:0.6] loss binary cross-entropy, mean absolute error epochs 200, 500 patience 200 epochs batch 10, 50 weight initializer Glorot normal, autoencoder no. layers frozen 0, 1, 2

G: number of genes

5 Table S4: Subtype neural network hyperparameters. model no. models hyperparameter values neural network 90 hidden layers 3 hidden nodes [4000-125] architectures 5 optimizer Nadam, Adadelta activation sigmoid, tanh, relu dropout [0.4:0.2:0.8] loss categorical cross-entropy epochs 200 batch 10 weight initializer Glorot normal

6 Model performance

Autoencoder

Figure S2: Performance of transcriptomic autoencoder in (top) 10-fold cross-validation grid search, and (bottom) R2 distribution after retraining. Architecture refers to the hidden nodes in the three encoding layers.

opt_act

0.4 ● Adadelta_relu Adadelta_sigmoid Adadelta_tanh 0.3 Nadam_relu Nadam_sigmoid ● ● ●● Nadam_tanh val_r2 0.2

archit

0.1 ● 1000_500_250_0_0 ● 2000_1000_500_0_0 ● 3000_1500_750_0_0 0.0 ● 4000_2000_1000_0_0

0.0 0.2 0.4 0.6 r2

1500

1000 count

500

0.0 0.2 0.4 0.6 0.8 r2

7 Radiogenomic models

Table S5: Radiogenomic model performances. For each model, the best performing hyperparameters were selected based on cross-validation AUC, and their results are shown here.

label name nn gbt rf svm logit f5 enhancing 0.722 0.511 0.542 0.571 0.538 f6 nCET 0.826 0.722 0.721 0.667 0.617 f7 necrosis 0.751 0.638 0.627 0.607 0.553 AUC f14 edema 0.784 0.673 0.647 0.613 0.517 f10 inﬁltrative 0.780 0.492 0.545 0.617 0.573 f9 focal 0.849 0.728 0.697 0.694 0.650 f5 enhancing - 0.211 0.180 0.151 0.184 f6 nCET - 0.104 0.105 0.159 0.209 f7 necrosis - 0.113 0.124 0.144 0.198 ∆AUC (nn - another model) f14 edema - 0.111 0.137 0.171 0.267 f10 inﬁltrative - 0.288 0.235 0.163 0.207 f9 focal - 0.121 0.152 0.155 0.199

nCET (non-contrast enhancing tumor), neural network (nn), gradient boosted trees (gbt), random forest (rf), support vector machines (svm), logistic regression (logit)

8 Subtype neural network

Table S6: Cross-validation results of selected hyperparameters for subtype neural network. Values are in AUC and averaged over 10 folds. Individual subtype AUCs were calculated based on one-versus-others.

classical mesenchymal neural proneural micro-averaged macro-averaged training 0.9965 0.9980 1 0.9974 0.9964 0.9983 validation 0.9841 0.9974 1 0.9910 0.9938 0.9956

Table S7: Performance scores of subtype model in gene masking with various gene sets, corre- sponds to Fig. 5 in main text. Subtype gene sets were deﬁned by [28]. Random gene sets were obtained by random sampling of genes, excluding subtypes genes. The fully trained subtype model had perfect classiﬁcation, as expected. The model was able to retain high performance scores when only using subtype genes.

gene set gene size AUC f1-score average precision random 100 0.741 0.485 0.528 random 200 0.829 0.643 0.676 mesenchymal 216 0.897 0.725 0.789 all subtypes 840 0.994 0.930 0.984

9 Bootstrapped performances

Figure S3: Comparison of neural network performance compared to other models in 100 bootstrapped datasets. Points centered below the diagonal line indicate cases where neural networks had better 10-fold cross-validation performance, and was the case for all bootstrapped datasets. For each bootstrap, the difference was equal to the neural network performance mi- nus another model’s performance. Notation - gbt: gradient boosted trees, rf: random forest, svm: support vector machines, logit: logistic regression.

gbt rf svm logit 1.0

0.8 enhancing

0.6

0.4

0.2 1.0

0.8 nCET 0.6

0.4

0.2 1.0

0.8 necrosis

0.6

0.4 difference other 0.6 0.2 model 0.4 mean 1.0 AUC 0.2 0.8 0.0 edema

0.6

0.4

0.2 1.0

0.8 infiltrative

0.6

0.4

0.2 1.0

0.8 focal 0.6

0.4

0.2 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 neural network mean AUC

10 Figure S4: Distribution of 10-fold cross-validation performances in 100 bootstrapped datasets across models. Dashed vertical lines represent 95% confidence intervals (CI). Solid vertical lines indicate 0.5 AUC - classification was as good as random. Confidence intervals did not overlap between neural networks and any other model and suggested neural networks were better models.

enhancing nCET necrosis edema infiltrative focal

1.0

0.9

● ● 0.8 ● ● ● model ● ● 0.7 ● nn ● ● ● ● ● ● ● ● gbt mean ● AUC rf 0.6 svm logit 0.5 ● ● ● ● ● ● 0.4 ●

● ● ● ● ● 0.3 ●

enhancing nCET necrosis edema infiltrative focal 20 15 10 5 0 20 15 10 5 0 model 20 nn 15 gbt count 10 rf 5 svm 0 20 logit 15 10 5 0 20 15 10 5 0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 mean AUC

11 Figure S5: Distribution of training and validation of neural networks in the 100 bootstrapped datasets. The 10-fold cross-validation means: (a) number of epochs (b) loss, (c) AUC, (d) average precision. Values were recorded at the epoch where highest performance was reached in early stopping. Grey intervals show the standard deviation of a value within a 10-fold cross- validation. Bootstraps were sorted by validation values for visualization purposes.

training validation 1.1 0.9 0.7 0.5

1.1 0.9 0.7 0.5

1.1 0.9

0.7 label 0.5 enhancing nCET 1.1 necrosis edema 0.9 infiltrative

mean accuracy focal 0.7 0.5

1.1 0.9 0.7 0.5

0 25 50 75 100 0 25 50 75 100 bootstraps

12 Gene masking

As gene set size increased, both AP and AUC increased. Although the full input, i.e., 12,042 gene expressions, was much larger, the models mapped multiple subsets of the input without losing a large proportion of classiﬁcation performance. This was likely reﬂected the correlative nature between gene expressions, e.g., previous work with neural networks from Chen et al. was able to use landmark genes to predict another 9520 target genes [25].

Subtype neural network

Figure S6: Subtype gene set masking with subytpe model. (top) Model’s probabilities and, (bottom) model performances. Subtypes and their gene sets were taken from [28]. Random genes were randomly sampled and did not overlap with subtype genes.

random 200 genes all subtype genes CL genes MES genes NL genes PN genes CL

MES

probability 1.0

0.8 truth 0.6 NL 0.4

0.2

0.0

CL MES NL PN CL MES NL PN CL MES NL PN CL MES NL PN CL MES NL PN CL MES NL PN model prediction average precision AUC 1 gene set NL NL 0.9 verhaak random all all 0.8 MES MES 0.7 num. genes (50,100] 0.6 CL CL (100,150] 0.5 (150,200] PN PN 0.4 (200,250] (800,850] random 100 random 100 0.3 random 200 random 200 0.2 % coverage

gene set gene num. genes % coverage Neural Classical Mesenchymal Proneural set gene num. genes % coverage Neural Classical Mesenchymal Proneural (90,100] 0.1 0

13 Figure S7: Perturbation of the subtype model with gene sets describing cell types and phenotypes [39, 38, 37], downloaded from Puchalski et al. [40].(top) Model performance in gene set masking and (bottom) gene set enrichment in genes ranked by single gene masking. For comparison, see the paper’s Fig. S6. Neither rows nor columns were clustered in order mtach the order in the paper. Cell type enrichment for subtypes are similar to the gene masking scores for neural, proneural and mesenchymal ﬁndings in the paper. A major difference includes the subtype neural network’s ability to predict with high AUC and precision the mesenchymal subtype with the GBM core astrocyte gene set, but this was not shown in the paper. The study found neural and proneural subtypes are often enriched by astroctye gene sets; mesenchymal associated with endothelial cells; and proneural with oligodendrocytes and quiescent fetal neurons. However, the associations between the study’s and the neural network’s gene masking do not completely agree. This disagreement was likely due to different goals: the study measures gene set associations based on single sample gene set enrichment analysis and gene masking was based on classiﬁcation performance. Although, the overlap does show consistency between GBM subtypes and brain cell types, suggested that cell types were both enriched and predictive of the subtypes.

average precision AUC 1 Zhang matureastrocytes Zhang matureastrocytes gene set puchalski Zhang neuron Zhang neuron 0.9 Zhang oligodendrocytes Zhang oligodendrocytes 0.8 num. genes Darmanis astrocytes Darmanis astrocytes 0.7 (10,20] (20,30] Darmanis mixOPCOligNeurons Darmanis mixOPCOligNeurons 0.6 (30,40] Zhang endothelial Zhang endothelial 0.5 (40,50] Patel immune Patel immune 0.4 (50,60] Zhang microgliamacrophages Zhang microgliamacrophages 0.3 Patel anticellcycle Patel anticellcycle % coverage (30,40] Patel hypoxia Patel hypoxia 0.2 (60,70] 0.1 Zhang GBMcoreastrocytes Zhang GBMcoreastrocytes (70,80] Zhang fetalastrocytes Zhang fetalastrocytes 0 (80,90] Darmanis Endothelial Darmanis Endothelial (90,100] Darmanis FetalNeuronsquiescent Darmanis FetalNeuronsquiescent Darmanis FetalNeuronsreplicating Darmanis FetalNeuronsreplicating Darmanis Neurons Darmanis Neurons Darmanis OPCs Darmanis OPCs Darmanis Oligodendrocytes Darmanis Oligodendrocytes Darmanis microglia Darmanis microglia Darmanis mixNeuronsAstrocytes Darmanis mixNeuronsAstrocytes Patel cellcycle Patel cellcycle gene set gene num. genes % coverage Neural Proneural Classical Mesenchymal set gene num. genes % coverage Neural Proneural Classical Mesenchymal

14 Figure S8: Perturbation of the subtype model with hallmark gene sets [45]. Model performance in gene set masking. Shown are the top 20 hallmarks for each subtype ranked by average precision, totaling 32 gene sets.

average precision AUC 1 androgen response androgen response gene set adipogenesis adipogenesis 0.9 hallmark fatty acid metabolism fatty acid metabolism 0.8 num. genes bile acid metabolism bile acid metabolism (0,100] peroxisome peroxisome 0.7 (100,200] spermatogenesis spermatogenesis 0.6 xenobiotic metabolism xenobiotic metabolism 0.5 % coverage estrogen response late estrogen response late (80,90] 0.4 myogenesis myogenesis (90,100] estrogen response early estrogen response early 0.3 heme metabolism heme metabolism uv response up uv response up 0.2 apical junction apical junction 0.1 glycolysis glycolysis 0 uv response dn uv response dn inflammatory response inflammatory response kras signaling dn kras signaling dn complement complement hypoxia hypoxia mitotic spindle mitotic spindle p53 pathway p53 pathway il2 stat5 signaling il2 stat5 signaling apoptosis apoptosis kras signaling up kras signaling up dna repair dna repair oxidative phosphorylation oxidative phosphorylation protein secretion protein secretion pi3k akt mtor signaling pi3k akt mtor signaling tnfa signaling via nfkb tnfa signaling via nfkb interferon gamma response interferon gamma response g2m checkpoint g2m checkpoint wnt beta catenin signaling wnt beta catenin signaling gene set gene num. genes % coverage Classical Neural Mesenchymal Proneural set gene num. genes % coverage Classical Neural Mesenchymal Proneural

15 Radiogenomic neural networks

The 22 queried GBM genes from [29, 41] were AKT3, CCND2, CDK4, CDK6, CDKN2A, CDKN2B, CDKN2C, EGFR, ERBB2, IDH1, MDM2, MDM4, MET, MYCN, NF1, PARK2, PDGFRA, PIK3CA, PIK3R1, PTEN, RB1, TP53.

Figure S9: Hallmark gene set masking in radiogenomic models ranked by AUC with corresponding average precision. Shown are the top 10 genes per label.

AUC average precision 1 mitotic spindle mitotic spindle gene set dna repair dna repair 0.9 hallmark reactive oxigen species pathway reactive oxigen species pathway 0.8 num. genes complement complement (0,100] apoptosis apoptosis 0.7 (100,200] myc targets v1 myc targets v1 0.6 % coverage p53 pathway p53 pathway 0.5 (80,90] kras signaling dn kras signaling dn (90,100] estrogen response early estrogen response early 0.4 estrogen response late estrogen response late 0.3 xenobiotic metabolism xenobiotic metabolism glycolysis glycolysis 0.2 apical junction apical junction 0.1 heme metabolism heme metabolism myogenesis myogenesis 0 epithelial mesenchymal transition epithelial mesenchymal transition fatty acid metabolism fatty acid metabolism uv response up uv response up hypoxia hypoxia uv response dn uv response dn oxidative phosphorylation oxidative phosphorylation gene set gene num. genes % coverage focal infiltrative nCET necrosis enhancing edema set gene num. genes % coverage focal infiltrative nCET necrosis enhancing edema

16 Figure S10: Single gene masking in radiogenomic models ranked by (top) AUC with corresponding average precision values on the right and (bottom) average precision with corresponding AUC values on the right. Shown are the top 10 genes per label.

AUC average precision 1 SLC13A3 SLC13A3 subtype CYP46A1 CYP46A1 0.9 na CLDN10 CLDN10 unnamed TSC22D4 TSC22D4 0.8 NL CLDN8 CLDN8 0.7 PN C7orf16 C7orf16 MES CYP4A11 CYP4A11 0.6 SNTA1 SNTA1 CL GABRP GABRP 0.5 F3 F3 ZFY ZFY 0.4 NOC4L NOC4L 0.3 SPATA5L1 SPATA5L1 BBS4 BBS4 0.2 POLD2 POLD2 GAL GAL 0.1 HAVCR1 HAVCR1 0 CRMP1 CRMP1 WISP2 WISP2 EIF4EBP1 EIF4EBP1 CACNB2 CACNB2 RQCD1 RQCD1 HDAC11 HDAC11 PIK3IP1 PIK3IP1 HOXD3 HOXD3 ANXA2 ANXA2 RAI2 RAI2 MTSS1 MTSS1 KIAA1199 KIAA1199 MAB21L1 MAB21L1 DDB2 DDB2 POSTN POSTN DPYSL4 DPYSL4 B4GALT6 B4GALT6 RPS6KA6 RPS6KA6 IL22 IL22 REG3A REG3A SNTB1 SNTB1 MEF2B MEF2B C19orf24 C19orf24 ZBTB48 ZBTB48 ZNF528 ZNF528 IL1RAPL2 IL1RAPL2 CHRM2 CHRM2 C10orf6 C10orf6 PDCD4 PDCD4 FCHSD2 FCHSD2 SGPL1 SGPL1 DHTKD1 DHTKD1 NAP1L3 NAP1L3 MUPCDH MUPCDH SLC10A1 SLC10A1 YIPF1 YIPF1 C14orf172 C14orf172 PRTN3 PRTN3 CCL16 CCL16 GABRB2 GABRB2 PPM1H PPM1H INTS3 INTS3 KIAA0460 KIAA0460 CHD9 CHD9 subtype focal nCET edema necrosis enhancing infiltrative subtype focal nCET edema necrosis enhancing infiltrative

average precision AUC 1 CCPG1 CCPG1 subtype ABCB1 ABCB1 0.9 na VASH1 VASH1 unnamed DMPK DMPK 0.8 NL SPG7 SPG7 0.7 PN DDR1 DDR1 MES SDC3 SDC3 0.6 CL NTRK3 NTRK3 DHTKD1 DHTKD1 0.5 ALDH4A1 ALDH4A1 0.4 FAT FAT PGM5 PGM5 0.3 DAG1 DAG1 NKX2−5 NKX2−5 0.2 HIG2 HIG2 0.1 IMPACT IMPACT SLC13A3 SLC13A3 0 PCDH9 PCDH9 KIAA0141 KIAA0141 MME MME F3 F3 SPATA5L1 SPATA5L1 ZNF175 ZNF175 GABRP GABRP POMGNT1 POMGNT1 ZBTB48 ZBTB48 DNAJC17 DNAJC17 P18SRP P18SRP MAN2C1 MAN2C1 PRTN3 PRTN3 ZNF250 ZNF250 FIG4 FIG4 TMEM35 TMEM35 CAPN2 CAPN2 LAMA5 LAMA5 ANXA2 ANXA2 ETNK2 ETNK2 MTSS1 MTSS1 DCN DCN VANGL1 VANGL1 CACNB4 CACNB4 KLHDC3 KLHDC3 CACNB2 CACNB2 FAM63A FAM63A TRPM3 TRPM3 PACSIN3 PACSIN3 ABHD9 ABHD9 TFR2 TFR2 LOC4951 LOC4951 ZAK ZAK GSTA3 GSTA3 RIG RIG MAP3K9 MAP3K9 RBM28 RBM28 INTS9 INTS9 CAD CAD B4GALT6 B4GALT6 TDRD1 TDRD1 SNTB1 SNTB1 IL22 IL22 subtype focal enhancing edema necrosis nCET infiltrative subtype focal enhancing edema necrosis nCET infiltrative

17 Figure S11: Gene masking using gene sets associated with GBM genomic abnormalities [29, 41]. Shown are the top 15 gene sets ranked by average precision (AP) for (a) enhancing, (b) edema, (c) nCET, (d) necrosis, (e) focal, and (f) inﬁltrative neural networks. Only the collections from GO, motif, and canonical pathways were considered. There were 22 genes queried, where gene sets may involve more than one of the queried genes. a enhancing AP b edema AP 1 1 transmembrane receptor protein gene set central nervous gene set tyrosine kinase signaling pathway GO system neuron differentiation GO 0.8 transmembrane receptor protein 0.8 regulation of hormone levels num. genes tyrosine kinase signaling pathway num. genes leukocyte activation (300,400] regulation of cell activation (100,200] 0.6 0.6 (400,500] negative regulation of (200,300] lymphocyte activation (300,400] % coverage immune system process (400,500] immune effector process 0.4 (70,80] response to growth factor 0.4 response to alcohol (80,90] positive regulation of locomotion % coverage 0.2 (90,100] 0.2 (70,80] response to steroid hormone immune effector process (80,90] query protein heterodimerization activity 0 TP53 monovalent inorganic cation transport 0 query cellular response to RB1 EGFR organic cyclic compound EGFR urogenital system development PTEN activation of immune response PTEN single organism cell adhesion CDKN2A PIK3CA PIK3CA growth PIK3R1 autophagy MET signal transduction PARK2 regulation of multi organism process CDK6 by protein phosphorylation PARK2 regulation of homotypic protein serine threonine kinase activity cell cell adhesion positive regulation of kinase activity epithelial cell differentiation sensory organ development stem cell differentiation gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

c nCET AP d necrosis AP 1 gene set 1 gene set vasculature development gland development GO GO 0.8 reactome 0.8 negative regulation of phosphorylation negative regulation of phosphorylation num. genes num. genes regulation of protein (100,200] regulation of protein complex assembly serine threonine kinase activity 0.6 (200,300] 0.6 (200,300] regulation of mitotic cell cycle (300,400] regulation of homeostatic process (300,400] organelle assembly 0.4 (400,500] rhythmic process 0.4 (400,500] regulation of cellular response % coverage negative regulation % coverage to growth factor stimulus 0.2 (60,70] of transferase activity 0.2 (60,70] urogenital system development (70,80] regulation of hormone levels (70,80] (80,90] (80,90] glycerophospholipid metabolic process 0 negative regulation of locomotion 0 query query small molecule catabolic process TP53 phosphoric ester hydrolase activity TP53 response to radiation RB1 apoptotic signaling pathway RB1 EGFR EGFR negative regulation of cell cycle PTEN wnt signaling pathway PTEN cell cell junction CDK4 regulation of protein complex assembly CDKN2A CDKN2B NF1 positive regulation response to acid chemical of peptidase activity PARK2 reproductive system development dephosphorylation developmental biology regulation of lipid metabolic process gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

e focal AP f infiltrative AP 1 gene set 1 gene set synaptic signaling wnt signaling pathway GO GO 0.8 0.8 protein heterodimerization activity num. genes regulation of protein complex assembly num. genes cellular response to (200,300] spinal cord development (100,200] organic cyclic compound 0.6 0.6 (300,400] positive regulation of (200,300] response to steroid hormone (400,500] protein complex assembly (300,400] (400,500] negative regulation of transport 0.4 % coverage negative regulation of transport 0.4 response to growth factor (70,80] response to drug % coverage 0.2 (80,90] 0.2 (60,70] vasculature development ligase activity (70,80] query (80,90] monovalent inorganic cation transport 0 TP53 response to inorganic substance 0 (90,100] RB1 regulation of anatomical structure size EGFR response to oxygen levels query circulatory system process PTEN developmental growth TP53 NF1 PTEN negative regulation of intracellular signal transduction PIK3CA hemostasis CDKN2A muscle system process PARK2 positive regulation of NF1 cellular component biogenesis PIK3CA secretion by cell transcription factor complex MDM2 cellular response to lipid wound healing ubiquitin like regulation of cytoplasmic transport protein transferase activity gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

18 Figure S12: Comparison of enhancement associations found in previous studies. Radiogenomic models were masked with gene sets queried from the Molecular Signature Database (MSigDB) based on key words from published findings. (a) Enhancement was found to be associated with hypoxia, ECM, and angiogenesis gene modules from Diehn et al. (n=22) [2]. There were 15 gene sets returned from querying hypoxia, ECM, and angiogenesis. (b) Jamshidi et al. found associations between 17 Biocarta pathways and enhancement [n=23][10].(c) Jamshidi et al. also stated C1orf172, CAMSAP2, KCNK3, LTBP1 genes were related to enhancement. There were 114 gene sets containing the four genes (d) Gutman et al. found a non-significant correlation between EGFR copy number amplification and enhancement (n=75) [7]. Querying MSigDB for EGFR resulted in 257 gene sets. The top ten gene sets ranked by the average precision (AP) in predicting enhancement were kept for (b-c).

a average precision AUC 1 gene set naba proteoglycans naba proteoglycans 0.9 GO naba collagens naba collagens 0.8 kegg biocarta ecm receptor interaction ecm receptor interaction 0.7 canonical 0.6 hif pathway hif pathway 0.5 num. genes regulation of cell migration regulation of cell migration involved in sprouting angiogenesis involved in sprouting angiogenesis 0.4 (0,100] (100,200] ecm pathway ecm pathway 0.3 (200,300] 0.2 naba basement membranes naba basement membranes (300,400] 0.1 vegf pathway vegf pathway % coverage 0 p53hypoxia pathway p53hypoxia pathway (50,60] regulation of transcription from regulation of transcription from (60,70] rna polymerase ii promoter rna polymerase ii promoter (70,80] naba ecm glycoproteins naba ecm glycoproteins (80,90] (90,100] naba core matrisome naba core matrisome query naba ecm regulators naba ecm regulators hypoxia naba secreted factors naba secreted factors ECM angiogenesis naba ecm affiliated naba ecm affiliated gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

b average precision AUC 1 nfat pathway nfat pathway gene set 0.9 biocarta mapk pathway mapk pathway 0.8 toll pathway toll pathway num. genes 0.7 met pathway met pathway (20,30] 0.6 (30,40] agr pathway agr pathway 0.5 (40,50] pdgf pathway pdgf pathway 0.4 (50,60] egf pathway egf pathway (80,90] 0.3 biopeptides pathway biopeptides pathway 0.2 % coverage at1r pathway at1r pathway 0.1 (80,90] tff pathway tff pathway (90,100]

gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema 0 query named

c average precision AUC 1 gene set monovalent inorganic cation transport monovalent inorganic cation transport 0.9 GO protein heterodimerization activity protein heterodimerization activity 0.8 num. genes passive transmembrane passive transmembrane 0.7 (200,300] transporter activity transporter activity 0.6 (300,400] growth growth 0.5 (400,500]

divalent inorganic cation homeostasis divalent inorganic cation homeostasis 0.4 % coverage protein serine threonine kinase activity protein serine threonine kinase activity 0.3 (70,80] 0.2 (80,90] gated channel activity gated channel activity 0.1 query cation channel activity cation channel activity 0 C1orf172 metal ion metal ion KCNK3 transmembrane transporter activity transmembrane transporter activity LTBP1 sensory organ development sensory organ development gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

d average precision AUC 1 gene set response to steroid hormone response to steroid hormone 0.9 motif protein heterodimerization activity protein heterodimerization activity 0.8 GO cellular response to cellular response to 0.7 num. genes organic cyclic compound organic cyclic compound 0.6 (300,400] sensory organ development sensory organ development 0.5 (400,500] transmembrane receptor protein transmembrane receptor protein tyrosine kinase signaling pathway tyrosine kinase signaling pathway 0.4 % coverage response to alcohol response to alcohol 0.3 (70,80] 0.2 (80,90] positive regulation of kinase activity positive regulation of kinase activity 0.1 (90,100] actgtga mir27a mir27b actgtga mir27a mir27b 0 query signal transduction signal transduction EGFR by protein phosphorylation by protein phosphorylation protein serine threonine kinase activity protein serine threonine kinase activity gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

19 Figure S13: Comparison of edema associations found in Zinn et al. [n=78][6]. Radiogenomic models were masked with gene sets queried from the MSigDB based on key words from published findings. Zinn et al. found concordant changes in mRNA and microRNA fold changes for patients with high edema or invasive traits: (a) The top five upregulated microRNA and their associated genes and (b) top five upregulated genes in patients with high FLAIR volumes, see Table 4 of the authors’ paper. This resulted in 343 and 160 related MSigDB gene sets, respectively. Shown are the top ten gene sets ranked by average precision (AP) in predicting edema in our study.

a average precision AUC 1 gene set postsynapse postsynapse 0.9 motif actin cytoskeleton actin cytoskeleton 0.8 GO 0.7 e4f1 q6 e4f1 q6 num. genes 0.6 (200,300] anchoring junction anchoring junction 0.5 (300,400] (400,500] cactgtg mir128a mir128b cactgtg mir128a mir128b 0.4 0.3 % coverage taxis taxis 0.2 (60,70] single organism cell adhesion single organism cell adhesion 0.1 (70,80] (80,90] 0 stat4 01 stat4 01 query gtttgtt mir495 gtttgtt mir495 MPPED2 CTNNA2 wggaatgy tef1 q6 wggaatgy tef1 q6 PKP4 gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema KIF5C

b average precision AUC 1 gene set positive regulation of locomotion positive regulation of locomotion 0.9 motif cellular response to lipid cellular response to lipid 0.8 GO 0.7 response to growth factor response to growth factor num. genes 0.6 (300,400] response to drug response to drug 0.5 (400,500] taxis taxis 0.4 % coverage 0.3 (60,70] response to inorganic substance response to inorganic substance 0.2 (70,80] ctacctc let7a let7b let7c let7d ctacctc let7a let7b let7c let7d 0.1 (80,90] let7e let7f mir98 let7g let7i let7e let7f mir98 let7g let7i (90,100] 0 muscle structure development muscle structure development query transmembrane receptor protein transmembrane receptor protein tyrosine kinase signaling pathway tyrosine kinase signaling pathway POSTN negative regulation of negative regulation of CXCL12 immune system process immune system process COL1A1 gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema COL6A3 GRB10

20 Figure S14: Comparison of necrosis associations found in previous studies. Radiogenomic models were masked with gene sets queried from the MSigDB based on key words from published ﬁndings. (a) Gevaert et al. reported the boundary sharpness of necrosis region was associated with GAP43, WWTR1, IL4 pathway, and cell membrane genes [n=55][9]. This returned 378 gene sets from MSigDB. (b) Jamshidi et al. found four Biocarta pathways (named gene sets) and (c) two genes associated with necrosis [10].(d) Gutman et al. found necrosis was correlated with the deletion of CDKN2A, but was not signiﬁcant [7]. An MSigDB query for the gene returned 183 gene sets. The top ten gene sets ranked by the average precision in predicting necrosis were kept in (a,b,d).

a average precision AUC 1 gene set growth factor activity growth factor activity 0.9 GO 0.8 regulation of sequence specific regulation of sequence specific num. genes dna binding transcription factor activity dna binding transcription factor activity 0.7 (100,200] leukocyte cell cell adhesion leukocyte cell cell adhesion 0.6 (200,300] 0.5 (300,400] negative regulation of phosphorylation negative regulation of phosphorylation 0.4 (400,500] negative regulation negative regulation 0.3 % coverage of transferase activity of transferase activity 0.2 (70,80] regulation of leukocyte proliferation regulation of leukocyte proliferation 0.1 (80,90] 0 query apoptotic signaling pathway apoptotic signaling pathway IL4 WWTR1 negative regulation of kinase activity negative regulation of kinase activity

regulation of response to wounding regulation of response to wounding

negative regulation of locomotion negative regulation of locomotion gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

b average precision AUC 1 tid pathway tid pathway gene set 0.9 biocarta pml pathway pml pathway 0.8 cytokine pathway cytokine pathway 0.7 num. genes (10,20] ck1 pathway ck1 pathway 0.6

gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema 0.5 (20,30] 0.4 % coverage 0.3 0.2 (90,100] 0.1 query 0 c average precision AUC named 1 gene set leukocyte cell cell adhesion leukocyte cell cell adhesion 0.9 motif pea3 q6 pea3 q6 0.8 GO 0.7 stat4 01 stat4 01 num. genes 0.6 (200,300] lef1 q2 lef1 q2 0.5 (400,500] 0.4 lbp1 q6 lbp1 q6 % coverage 0.3 (60,70] regulation of regulation of epithelial cell proliferation epithelial cell proliferation 0.2 (70,80] (80,90] crx q4 crx q4 0.1 0 query actgtga mir27a mir27b actgtga mir27a mir27b ITGA5 cebpb 01 cebpb 01 RUNX3 cacccbindingfactor q6 cacccbindingfactor q6 gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

d average precision AUC 1 regulation of protein regulation of protein gene set serine threonine kinase activity serine threonine kinase activity 0.9 GO 0.8 negative regulation of phosphorylation negative regulation of phosphorylation num. genes 0.7 (100,200] negative regulation negative regulation of transferase activity of transferase activity 0.6 (200,300] 0.5 (300,400] regulation of mitotic cell cycle regulation of mitotic cell cycle 0.4 (400,500] regulation of sequence specific regulation of sequence specific dna binding transcription factor activity dna binding transcription factor activity 0.3 % coverage positive regulation positive regulation 0.2 (70,80] of peptidase activity of peptidase activity 0.1 (80,90] regulation of leukocyte proliferation regulation of leukocyte proliferation 0 query CDKN2A positive regulation of proteolysis positive regulation of proteolysis

protein maturation protein maturation

negative regulation of kinase activity negative regulation of kinase activity gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

21 Figure S15: Comparison of expansiveness versus infiltrative associations found in previous studies. Radiogenomic models were masked with gene sets queried from the MSigDB based on key words from published findings. (a) Colen et al. found invasive tumors to be associated with MYC, leading to NFKBIA inhibition [n=104][48]. Querying MSigDB for MYC and NFKBIA resulted in 316 gene sets. (b) Diehn et al. looked at infiltrative vs. edematous T2 abnormality and found an association with an immune cell gene module [2]. An MSigDB query for ‘immune’ returned 87 gene sets. Shown are the top ten gene sets ranked by average precision (AP) in predicting infiltrative tumors in our study.

a average precision AUC 1 gene set negative regulation of transport negative regulation of transport 0.9 motif 0.8 GO response to drug response to drug 0.7 num. genes 0.6 (100,200] wnt signaling pathway wnt signaling pathway 0.5 (200,300] 0.4 (300,400] positive regulation of proteolysis positive regulation of proteolysis (400,500] 0.3 0.2 % coverage ap4 q6 01 ap4 q6 01 0.1 (70,80] (80,90] 0 amide binding amide binding (90,100]

er q6 er q6 query MYC NFKBIA pax2 02 pax2 02

ubiquitin like protein ligase binding ubiquitin like protein ligase binding

cebp c cebp c gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

b average precision AUC 1 gene set regulation of immune effector process regulation of immune effector process 0.9 GO 0.8 negative regulation of negative regulation of num. genes immune system process immune system process 0.7 (0,100] 0.6 (100,200] regulation of innate immune response regulation of innate immune response 0.5 (200,300] 0.4 (300,400] response to corticosteroid response to corticosteroid (400,500] 0.3 0.2 % coverage granulocyte differentiation granulocyte differentiation 0.1 (60,70] (70,80] innate immune response activating innate immune response activating 0 cell surface receptor signaling pathway cell surface receptor signaling pathway (80,90] (90,100] activation of innate immune response activation of innate immune response query positive regulation of positive regulation of immune innate immune response innate immune response negative regulation of negative regulation of immune effector process immune effector process

regulation of type 2 immune response regulation of type 2 immune response gene set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema set gene num. genes % coverage query enhancing nCET necrosis focal infiltrative edema

22 Figure S16: Perturbation of radiogenomic models with subtype gene sets from [28].(top) Model performance in gene set masking, and (bottom) gene set enrichment in ranked genes after single gene masking. The authors found mesenchymal tumors had less nCET and proneural tumors had less enhancement. However, the radiogenomic models found neural genes were more predictive of nCET than mesenchymal genes (0.64 vs. 0.55 AUC, 0.52 vs. 0.46 AP) and other subtype genes were more predictive than proneural genes in predicting enhancement.

average precision AUC 1 gene set MES MES 0.9 verhaak 0.8 PN PN num. genes 0.7 (100,150] NL NL 0.6 (150,200] 0.5 (200,250]

CL CL 0.4 % coverage gene set gene num. genes % coverage enhancing edema focal necrosis nCET infiltrative set gene num. genes % coverage enhancing edema focal necrosis nCET infiltrative 0.3 (90,100] 0.2 0.1 0

23 Figure S17: Perturbation of radiogenomic models with gene sets from brain cell types and phenotypes [39, 38, 37], downloaded from Puchalski et al. [40].(top) Gene set masking, where the top 20 ranked by average precision were kept; shown are 21 gene sets. (bottom) GSEA of genes ranked in single gene masking. Shown are gene sets with at least one signiﬁcant enrichment.

average precision AUC 1 Zhang neuron Zhang neuron gene set Zhang GBMcoreastrocytes Zhang GBMcoreastrocytes 0.9 puchalski Zhang endothelial Zhang endothelial 0.8 num. genes Zhang matureastrocytes Zhang matureastrocytes (10,20] Darmanis microglia Darmanis microglia 0.7 (20,30] Darmanis astrocytes Darmanis astrocytes 0.6 (30,40] Zhang oligodendrocytes Zhang oligodendrocytes (40,50] Darmanis Oligodendrocytes Darmanis Oligodendrocytes 0.5 (50,60] Patel cellcycle Patel cellcycle 0.4 % coverage Darmanis OPCs Darmanis OPCs 0.3 (30,40] Darmanis FetalNeuronsquiescent Darmanis FetalNeuronsquiescent (60,70] Darmanis Neurons Darmanis Neurons 0.2 (70,80] Zhang microgliamacrophages Zhang microgliamacrophages 0.1 (80,90] Darmanis mixOPCOligNeurons Darmanis mixOPCOligNeurons (90,100] Patel immune Patel immune 0 Zhang fetalastrocytes Zhang fetalastrocytes Darmanis FetalNeuronsreplicating Darmanis FetalNeuronsreplicating Patel anticellcycle Patel anticellcycle Patel hypoxia Patel hypoxia Darmanis Endothelial Darmanis Endothelial Darmanis mixNeuronsAstrocytes Darmanis mixNeuronsAstrocytes gene set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative

normalized enrichment score adjusted p−value 3 p < 0.05 Zhang oligodendrocytes gene set Zhang oligodendrocytes puchalski Darmanis FetalNeuronsquiescent 2 Darmanis FetalNeuronsquiescent Patel hypoxia % coverage Patel hypoxia (60,70] 1 Zhang microgliamacrophages (70,80] Zhang microgliamacrophages Darmanis microglia (80,90] Darmanis microglia 0 Zhang matureastrocytes num. genes Zhang matureastrocytes Patel immune (10,20] Patel immune −1 Zhang GBMcoreastrocytes (20,30] Zhang GBMcoreastrocytes (30,40] Zhang endothelial −2 Zhang endothelial (40,50] Zhang neuron (50,60] Zhang neuron gene set gene % coverage num. genes enhancing focal necrosis edema nCET infiltrative −3 enhancing focal necrosis edema nCET infiltrative p >= 0.05

24 Figure S18: Gene masking in radiogenomic models with canonical gene sets from MSigDB. (top) Gene set masking, where the top 5 ranked by average precision were kept; shown are 21 gene sets. (bottom) GSEA of genes ranked in single gene masking. Shown are gene sets with at least one signiﬁcant enrichment.

average precision AUC 1 st t cell signal transduction st t cell signal transduction gene set canonical st integrin signaling pathway st integrin signaling pathway 0.9 pid ecadherin stabilization pathway pid ecadherin stabilization pathway num. genes pid il27 pathway pid il27 pathway 0.8 (0,100] (100,200] pid p53 downstream pathway pid p53 downstream pathway 0.7 pid ptp1b pathway pid ptp1b pathway (200,300] st fas signaling pathway st fas signaling pathway 0.6 (300,400] pid ps1 pathway pid ps1 pathway % coverage 0.5 pid telomerase pathway pid telomerase pathway (50,60] pid myc repress pathway pid myc repress pathway 0.4 (60,70] pid ap1 pathway pid ap1 pathway (70,80] pid reg gr pathway pid reg gr pathway 0.3 (80,90] pid fgf pathway pid fgf pathway (90,100] pid hif1 tfpathway pid hif1 tfpathway 0.2 pid lysophospholipid pathway pid lysophospholipid pathway 0.1 pid endothelin pathway pid endothelin pathway naba core matrisome naba core matrisome 0 wnt signaling wnt signaling naba ecm regulators naba ecm regulators naba secreted factors naba secreted factors naba ecm affiliated naba ecm affiliated gene set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative

normalized enrichment score adjusted p−value 4 gene set p < 0.05 cell cycle mitotic cell cycle mitotic canonical

translation % coverage translation 2 nonsense mediated decay enhanced by the exon junction complex (40,50] nonsense mediated decay enhanced by the exon junction complex (50,60] peptide chain elongation (70,80] peptide chain elongation 0 tca cycle and respiratory electron transport (80,90] tca cycle and respiratory electron transport (90,100] srp dependent cotranslational protein targeting to membrane srp dependent cotranslational protein targeting to membrane num. genes −2 influenza life cycle (0,100] influenza life cycle (100,200] influenza viral rna transcription and replication influenza viral rna transcription and replication (200,300] kegg ribosome −4 (300,400] kegg ribosome p >= 0.05 3 utr mediated translational regulation 3 utr mediated translational regulation formation of fibrin clot clotting cascade formation of fibrin clot clotting cascade response to elevated platelet cytosolic ca2 response to elevated platelet cytosolic ca2 pid hif1 tfpathway pid hif1 tfpathway circadian clock circadian clock kegg alzheimers disease kegg alzheimers disease bmal1 clock npas2 activates circadian expression bmal1 clock npas2 activates circadian expression gene set gene % coverage num. genes enhancing edema infiltrative nCET necrosis focal enhancing edema infiltrative nCET necrosis focal

25 Figure S19: Gene masking in radiogenomic models with motif gene sets from MSigDB. (top) Gene set masking, where the top 5 ranked by average precision were kept; shown are 29 gene sets. (bottom) GSEA of genes ranked in single gene masking. Shown are gene sets with at least one signiﬁcant enrichment.

average precision AUC 1 nfkb q6 nfkb q6 gene set wggaatgy tef1 q6 wggaatgy tef1 q6 0.9 motif cebpdelta q6 cebpdelta q6 0.8 num. genes s8 01 s8 01 0.7 (100,200] (200,300] rgaannttc hsf1 01 rgaannttc hsf1 01 0.6 aaayrnctg unknown aaayrnctg unknown (300,400] 0.5 actgtga mir27a mir27b actgtga mir27a mir27b (400,500] ctacctc let7a let7b let7c let7d ctacctc let7a let7b let7c let7d 0.4 let7e let7f mir98 let7g let7i let7e let7f mir98 let7g let7i % coverage cebp c cebp c 0.3 (60,70] ap2 q6 ap2 q6 0.2 (70,80] myb q3 myb q3 0.1 myb q5 01 myb q5 01 0 cactgtg mir128a mir128b cactgtg mir128a mir128b gata c gata c lmo2com 02 lmo2com 02 myb q6 myb q6 usf c usf c mmef2 q6 mmef2 q6 cdx2 q5 cdx2 q5 tttgtag mir520d tttgtag mir520d cccnngggar olf1 01 cccnngggar olf1 01 gtgccaa mir96 gtgccaa mir96 smad3 q6 smad3 q6 hsf1 01 hsf1 01 osf2 q6 osf2 q6 lmo2com 01 lmo2com 01 bach2 01 bach2 01 foxo4 02 foxo4 02 e4f1 q6 e4f1 q6 gene set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative

normalized enrichment score adjusted p−value 3 p < 0.05 e2f4dp1 01 gene set e2f4dp1 01 e2f1 q6 motif e2f1 q6 2 e2f 02 % coverage e2f 02 e2f4dp2 01 (60,70] e2f4dp2 01 1 e2f1dp1 01 (70,80] e2f1dp1 01 e2f1dp2 01 (80,90] e2f1dp2 01 0 actgtag mir139 num. genes actgtag mir139 gttaaag mir302b (0,100] gttaaag mir302b −1 yy1 02 (100,200] yy1 02 sp1 q6 01 (200,300] sp1 q6 01 −2 srebp1 q6 (300,400] srebp1 q6 catgtaa mir496 (400,500] catgtaa mir496 −3 p >= 0.05 etf q6 etf q6 ngfic 01 ngfic 01 lfa1 q6 lfa1 q6 rora1 01 rora1 01 ygcgyrcgc unknown ygcgyrcgc unknown cagtatt mir200b mir200c mir429 cagtatt mir200b mir200c mir429 mzf1 01 mzf1 01 atgtttc mir494 atgtttc mir494 tctgata mir361 tctgata mir361 ygcantgcr unknown ygcantgcr unknown aatgtga mir23a mir23b aatgtga mir23a mir23b tgaatgt mir181a mir181b mir181c mir181d tgaatgt mir181a mir181b mir181c mir181d atf1 q6 atf1 q6 atgttaa mir302c atgttaa mir302c tacttga mir26a mir26b tacttga mir26a mir26b cctgtga mir513 cctgtga mir513 myb q3 myb q3 gene set gene % coverage num. genes necrosis edema nCET focal enhancing infiltrative necrosis edema nCET focal enhancing infiltrative

26 Figure S20: Gene masking in radiogenomic models with chromosome gene sets from MSigDB. (top) Gene set masking, where the top 5 ranked by average precision were kept; shown are 25 gene sets. (bottom) GSEA of genes ranked in single gene masking. Shown are gene sets with at least one signiﬁcant enrichment.

average precision AUC 1 chr11q12 chr11q12 gene set chr18q11 chr18q11 0.9 chromosome chr18q21 chr18q21 0.8 num. genes chr5q31 chr5q31 (0,100] chr17p13 chr17p13 0.7 (100,200] chr1q21 chr1q21 0.6 (200,300] chr16p13 chr16p13 (300,400] chr10q21 chr10q21 0.5 (400,500] chr11p15 chr11p15 0.4 % coverage chr15q24 chr15q24 (30,40] chr2q31 chr2q31 0.3 (40,50] chr17p12 chr17p12 0.2 (50,60] chr12p12 chr12p12 chr3q21 chr3q21 0.1 chr21q22 chr21q22 0 chr4p16 chr4p16 chr7q22 chr7q22 chr1p32 chr1p32 chr1q23 chr1q23 chr15q15 chr15q15 chr17q25 chr17q25 chr17q21 chr17q21 chr10q23 chr10q23 chrxq22 chrxq22 chr8p11 chr8p11 gene set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative set gene num. genes % coverage nCET enhancing edema focal necrosis infiltrative

normalized enrichment score adjusted p−value 4 gene set p < 0.05 chr13q14 chromosome chr13q14

chr11q13 % coverage chr11q13 2 (20,30] chr4q25 (30,40] chr4q25 (40,50] chr8q11 0 chr8q11 num. genes chr16p11 (0,100] chr16p11 (100,200] −2 chr20q13 (200,300] chr20q13 chr16p13 (300,400] chr16p13 (600,700] chr8q24 −4 (900,1e+03] chr8q24 p >= 0.05

chr15q26 chr15q26

chr9q34 chr9q34

chr4q28 chr4q28

chr19q13 chr19q13

chr19p13 chr19p13

chr2q21 chr2q21 gene set gene % coverage num. genes enhancing infiltrative edema necrosis nCET focal enhancing infiltrative edema necrosis nCET focal

27 Figure S21: Gene masking in radiogenomic models with oncogenic signatures gene sets from MSigDB. (top) Gene set masking, where the top 5 ranked by average precision were kept; shown are 25 gene sets. (bottom) GSEA of genes ranked in single gene masking. Shown are gene sets with at least one signiﬁcant enrichment.

average precision AUC 1 mtor up.n4.v1 up mtor up.n4.v1 up gene set onco akt up mtor dn.v1 dn akt up mtor dn.v1 dn 0.9 jak2 dn.v1 up jak2 dn.v1 up 0.8 num. genes prc2 eed up.v1 dn prc2 eed up.v1 dn (100,200] 0.7 (200,300] atf2 s up.v1 dn atf2 s up.v1 dn 0.6 (400,500] stk33 nomo up stk33 nomo up rb p130 dn.v1 up rb p130 dn.v1 up 0.5 % coverage (60,70] atf2 up.v1 dn atf2 up.v1 dn 0.4 (70,80] pigf up.v1 up pigf up.v1 up 0.3 (80,90] cyclin d1 up.v1 up cyclin d1 up.v1 up (90,100] vegf a up.v1 up vegf a up.v1 up 0.2 brca1 dn.v1 dn brca1 dn.v1 dn 0.1 bmi1 dn.v1 up bmi1 dn.v1 up 0 prc2 eed up.v1 up prc2 eed up.v1 up pten dn.v2 dn pten dn.v2 dn kras.600 up.v1 up kras.600 up.v1 up kras.breast up.v1 up kras.breast up.v1 up atf2 s up.v1 up atf2 s up.v1 up ctip dn.v1 dn ctip dn.v1 dn kras.600.lung.breast up.v1 up kras.600.lung.breast up.v1 up il15 up.v1 dn il15 up.v1 dn kras.600 up.v1 dn kras.600 up.v1 dn rapa early up.v1 up rapa early up.v1 up nfe2l2.v2 nfe2l2.v2 pten dn.v1 up pten dn.v1 up gene set gene num. genes % coverage enhancing edema necrosis focal nCET infiltrative set gene num. genes % coverage enhancing edema necrosis focal nCET infiltrative

normalized enrichment score adjusted p−value 3 p < 0.05 bmi1 dn.v1 up gene set bmi1 dn.v1 up onco kras.lung.breast up.v1 dn 2 kras.lung.breast up.v1 dn stk33 dn % coverage stk33 dn (60,70] dca up.v1 dn 1 dca up.v1 dn (70,80] snf5 dn.v1 dn (80,90] snf5 dn.v1 dn atf2 s up.v1 dn 0 (90,100] atf2 s up.v1 dn il21 up.v1 dn il21 up.v1 dn num. genes −1 erb2 up.v1 up (0,100] erb2 up.v1 up kras.df.v1 up (100,200] kras.df.v1 up −2 bmi1 dn mel18 dn.v1 up (200,300] bmi1 dn mel18 dn.v1 up mel18 dn.v1 up mel18 dn.v1 up −3 p >= 0.05 crx dn.v1 dn crx dn.v1 dn esc v6.5 up early.v1 dn esc v6.5 up early.v1 dn mek up.v1 up mek up.v1 up kras.50 up.v1 up kras.50 up.v1 up lte2 up.v1 up lte2 up.v1 up mel18 dn.v1 dn mel18 dn.v1 dn kras.kidney up.v1 up kras.kidney up.v1 up pkca dn.v1 dn pkca dn.v1 dn cahoy oligodendrocutic cahoy oligodendrocutic crx nrl dn.v1 dn crx nrl dn.v1 dn gene set gene % coverage num. genes edema nCET focal necrosis enhancing infiltrative edema nCET focal necrosis enhancing infiltrative

28 Figure S22: Gene masking performance compared to (top) gene set size and (bottom) coverage. Coverage was the percent of genes in the gene set that was in input gene expressions. Average precision (left column) and AUC (right column) were used.

29 Figure S23: The trend between gene masking performance and gene set size broken down by gene set category: (top) average precision and (bottom) AUC.

30 Figure S24: The trend between gene masking performance and gene set coverage broken down by gene set category: (top) average precision and (bottom) AUC.

31 Gene saliency

Subtype neural network

Figure S25: Gene saliency in the subtype neural network. (a) Clustering of the enrichment scores between each patients (columns) and each subtype gene set (rows). Enrichments were significant at an adjusted p-value < 0.05. (b) The number of patients with significant enrichment in each subtype gene set based on the bottom heatmap in (a) and broken down by their true subtypes. Of the patients with the mesenchymal subtype, more patients (33 patients) were enriched with mesenchymal genes than any other subtype genes. To a lesser degree, the same was seen among patients with the classical subtype, where 32 patients were enriched with classical genes. In contrast, 49 patients in the proneural subtype were enriched with classical genes while 48 were enriched with proneural genes. These aforementioned associations between gene saliency and subtype overlap with the associations from single gene masking in Fig. 6b. As was seen in gene masking, genes from other subtypes were influential in predicting any individual subtype. In particular, neural patients’ salient genes were enriched more often with mesenchymal genes (22 patients) than with neural genes (14 patients). Likewise, proneural patients were similarly enriched with both classical and proneural genes. a normalized enrichment score

2.5 subtype subtype na CL 2 Neural 1.5 PN Proneural MES 1 Mesenchymal Classical NL 0.5 0 adjusted p−value p < 0.05 subtype CL PN MES NL p >= 0.05 50 b gene set 40 # 30 CL enriched 20 PN patients 10 MES NL 0 Classical Mesenchymal Neural Proneural patients' true subtype

32 Radiogenomic neural networks

Figure S26: Patients with signiﬁcant enrichment radiogenomic models based on gene saliency. Shown are other MSigDB gene set collections with at least 10 enriched patients. The chemical and genetic perturbations and computational collections were thresholded at 20 patients; all others were thresholded at 10 patients.

reactome

20 12

0 trafficking of ampa receptors onco

60 48 40

20 20 20 11 2 1 0 alk cahoy il15 radiogenomic dn.v1 neuronal up.v1 model up dn enhancing chem # nCET enriched necrosis patients 66 65 64 edema 60 55 55 56 53 51 focal 46 46 infiltrative 42 41 44 40

22 23 22 22 21 24 22 20 20 16 13 9 11 12 5 7 5 2 2 3 2 3 2 3 3 1 2 2 2 1 0 benporath blum croonquist dutertre elvidge elvidge elvidge elvidge fardin kang kobayashi martens meissner meissner mense mikkelsen mikkelsen mikkelsen mikkelsen rosty sotiriou prc2 response il6 estradiol hif1a hif1a hypoxia hypoxia hypoxia doxorubicin egfr tretinoin brain npc hypoxia mcv6 mef mef npc cervical breast targets to deprivation response and targets by up 11 resistance signaling response hcp hcp up hcp hcp hcp hcp cancer cancer salirasib dn 24hr hif2a dn dmog up 24hr up with with with with with with proliferation grade dn up targets up dn h3k27me3 h3 h3k27me3 h3 h3k27me3 h3k27me3 cluster 1 dn unmethylated unmethylated vs 3 up computational 69 63 66 60 55 55 42 44 40 39 33 36 36 28 29 21 23 23 20 20 20

1 1 3 2 1 1 4 1 1 1 1 1 3 2 1 2 1 2 0 gnf2 gnf2 gnf2 gnf2 gnf2 gnf2 gnf2 gnf2 gnf2 gnf2 gnf2 gnf2 module module module module module module card15 casp1 cd1d cd33 dnm1 fos hck mcl1 rab3a s100a4 tm4sf2 tnfrsf1b 289 316 375 64 84 99 gene sets

33 Survival

Ethnicity was removed since 98% of patients with radiogenomic data were non-Hispanic. Overall survival (OS) was defined in ’patient’ file. For patients without death events, days- to-last-followup were obtained by finding the maximum day among any reported days-to-event in the ‘patient’, ‘radiation’, ‘drug’, and ‘followup’ files. Progression-free survival (PFS) outcomes were defined by the ‘followup’ file, which included the event types: locoregional disease, metastatic, progression of disease, or recurrence. Un- known event types with days-to-event data were also removed unless other files stated patients had therapy regimes treated for progression, e.g., an unknown progression event type on day 554 and received radiation on day 576 to treat progression. For these cases (n=3), days-to- event data was kept, while event types were set to progression.

Figure S27: Survival estimates between TCGA-GBM (patients with transcriptome data), VASARI subset (patients with at least one MRI trait), and radiogenomic subset (patients with all MRI traits) in (a) overall survival (OS) and (b) progression-free survival (PFS). There were no differences in OS or PFS, see Supp. Table S8. Note: 27 of the overall cohort of 528 patients had missing survival and/or progression data, see Supp. Table S1. Similarly, 166 of 175 patient with any MRI trait also had survival information. Of the 166 patients, only 127 had all six MRI traits. a 1.00 + + TCGA−GBM b 1.00 ++ + TCGA−GBM ++ ++ + + VASARI subset + + VASARI subset + + + + + + ++ radiogenomic ++ radiogenomic 0.75 + 0.75 ++ +++ ++ +++ ++ + ++ ++ ++ OS ++ p = 0.23 PFS ++ p = 0.28 0.50 ++ 0.50 ++ probability + probability ++ + ++ + +++ + +++ + ++ 0.25 + 0.25 +++ ++ +++ ++ + ++++ ++++ + ++ ++++ ++ 0.00 + 0.00 + 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 years years Number at risk Number at risk 501 231 81 38 22 11 9 5 2 2 1 0 502 110 32 16 10 4 2 1 1 1 1 0 166 80 28 13 5 1 0 0 0 0 0 0 166 33 8 4 2 0 0 0 0 0 0 0 − 127 56 19 6 1 1 0 0 0 0 0 0 − 127 24 6 2 2 0 0 0 0 0 0 0 − 0 1 2 3 4 5 6 7 8 9 10 11 − 0 1 2 3 4 5 6 7 8 9 10 11 years years

Table S8: Summary measures of survival curves corresponding to cohorts in Supp. Figure S27.

cohort n events median (years) 95% CI (years) Overall survival TCGA-GBM 501 388 1.16 1.05, 1.24 VASARI subset 166 138 1.16 0.94, 1.26 radiogenomic 127 107 1.05 0.90, 1.27 Progression-free survival TCGA-GBM 502 331 0.70 0.64, 0.77 VASARI subset 166 115 0.66 0.52, 0.85 radiogenomic 127 88 0.54 0.46, 0.73

34 Figure S28: Survival estimates between patients split by their MRI traits. (a) Overall survival (OS) and (b) progression-free survival (PFS) differences.

1.00 + 1.00 + a + enhancing < 1/3 b ++ + enhancing < 1/3 ++ + + + enhancing >= 1/3 + enhancing >= 1/3 0.75 0.75 ++ +++ + +++ ++ OS ++ p = 0.41 PFS + p = 0.53 0.50 + 0.50 + probability + probability ++ + +++ 0.25 0.25 + + + ++ + + +++ +++ + 0.00 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 89 49 15 8 4 1 0 89 16 3 1 1 0 0

− 68 28 12 4 1 0 0 − 68 15 4 2 1 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years 1.00 1.00 ++ + nCET < 1/3 ++ + nCET < 1/3 + + + + nCET >= 1/3 + + nCET >= 1/3 0.75 + 0.75 + ++ + +++ + ++ OS ++ p = 0.62 PFS p = 0.77 0.50 0.50 + probability + probability +++ +++ 0.25 0.25 +++ + ++ ++ + + +++ ++ + 0.00 + 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 93 46 16 7 3 1 0 93 19 4 3 2 0 0

− 54 27 9 3 1 0 0 − 54 8 2 0 0 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years 1.00 + 1.00 ++ + + + + necrosis < 1/3 + necrosis < 1/3 + + + necrosis >= 1/3 + + necrosis >= 1/3 0.75 0.75 ++ +++++ + ++ ++ OS + p = 0.75 PFS + p = 0.54 0.50 ++ 0.50 ++ probability probability ++ +++ 0.25 + 0.25 ++ ++ + +++ ++ ++ 0.00 + 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 110 53 19 8 2 0 0 110 21 5 1 1 0 0

− 48 24 8 4 3 1 0 − 48 10 2 2 1 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years 1.00 1.00 ++ + focal ++ + focal + + + + non−focal + + non−focal 0.75 + 0.75 + ++ + + ++ ++ OS + p = 0.19 PFS p = 0.65 0.50 ++ 0.50 ++ probability probability + + +++ 0.25 0.25 + ++ ++ + + ++ + +++ + 0.00 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 128 65 26 13 5 1 0 128 25 6 3 2 0 0

− 24 11 2 0 0 0 0 − 24 5 1 0 0 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years 1.00 1.00 + + ++ + expansive + expansive + + ++ + + infiltrative + infiltrative 0.75 + 0.75 ++ ++ +++ ++ OS ++ p = 0.15 PFS + p = 0.033 0.50 + 0.50 + probability + probability +++ + +++ + 0.25 + 0.25 + ++ + ++ + + + + + 0.00 ++ 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 96 46 22 9 2 1 0 96 16 5 2 2 0 0

− 54 28 5 3 2 0 0 − 54 15 3 2 0 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years 1.00 + 1.00 + + edema < 1/3 ++ + edema < 1/3 + ++ + + + edema >= 1/3 + edema >= 1/3 0.75 +++ 0.75 ++ + ++ ++++ ++ OS ++ p = 0.53 PFS + p = 0.84 0.50 ++ 0.50 ++ probability + probability ++ + +++++ 0.25 + 0.25 ++ + ++ +++ + ++ 0.00 ++ 0.00 0 1 2 3 4 5 6 0 1 2 3 4 5 6 years years Number at risk Number at risk 81 37 13 7 2 0 0 81 12 2 0 0 0 0

− 72 36 13 4 2 1 0 − 72 16 5 3 2 0 0 − 0 1 2 3 4 5 6 − 0 1 2 3 4 5 6 years years 35 Figure S29: Frequency of enriched patients in each of the 54 radiogenomic traits. n = number of enriched patients

cell types or phenotypess chromosome hallmark reactome 80

60 edema 40 20 0 80 60 enhancing 40 20 0 80 60 focal 40 20 0 n 80

60 infiltrative 40 20 0 80 60 nCET 40 20 0 80

60 necrosis 40 20 0 hypoxia chr1p35 chr3p21 chr3p22 chr3q27 chr6q21 chr6q27 chr14q22 chr18p11 chr19p13 chr19q13 chr22q13 glycolysis e2f targets myogenesis p53 pathway Patel hypoxia Patel Patel immune Patel Zhang neuron myc targets v2 myc uv response up g2m checkpoint mtorc1 signaling tgf beta signaling allograft rejection allograft Patel anticellcycle Patel Zhang endothelial Zhang fetalastrocytes tnfa signaling via nfkb tnfa xenobiotic metabolism xenobiotic Zhang oligodendrocytes Zhang matureastrocytes interferon alpha response interferon Zhang gbmcoreastrocytes Darminis oligodendrocytes trafficking of ampa receptors trafficking Zhang microgliamacrophages Darminis fetalneuronsreplicating epithelial mesenchymal transition epithelial mesenchymal