MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes

Xiaoxiao Zhang; Maik Kschischo

doi:10.1371/journal.pone.0261183

Abstract

Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test set F₁ score >90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology.

Citation: Zhang X, Kschischo M (2021) MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes. PLoS ONE 16(12): e0261183. https://doi.org/10.1371/journal.pone.0261183

Editor: Tao Huang, Chinese Academy of Sciences, CHINA

Received: July 21, 2021; Accepted: November 24, 2021; Published: December 16, 2021

Copyright: © 2021 Zhang, Kschischo. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data used in this study are publicly available online. Detailed references to access the data can be found in the main text and Supplemental Information. We have also added a cloud folder at where all preprocessed data are available: https://cloud.hs-koblenz.de/s/ytFKkzcK78AekL4.

Funding: This work was supported by the FOR2800 research unit funded by the Deutsche Forschungsgemeinschaft (DFG project number 395736209).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Tumour-derived cell lines are important model systems for developing new anti-cancer treatments and for understanding cancer biology [1–3]. They are comparably cost efficient, easy to handle under laboratory conditions and do not inflict ethical issues arising in research involving human or animal subjects. Yet, promising cell line experiments are rarely translated to clinical applications. In some cases, there are remarkable differences between cell lines and the primary tumours they were derived from [2–4]. This is also the reason why the assignment of clinically informative tumour subtypes to cell line models [3–5] is not a straightforward task.

To narrow the gap between preclinical findings and tumour treatment, it is necessary to select appropriate cell line models for a given tumour sample or a given cancer subtype. Several attempts to evaluate similarities and differences between cell lines and bulk tumours have focused on associations between corresponding data modalities including mutation, copy number, gene expression and methylation [6–12]. An important data resource comes from collaborative projects like NCI-60 [13] and the Cancer Cell Line Encyclopaedia (CCLE) [5, 14], who have generated large-scale pharmacogenomics data from patient-derived cell lines across organs. Other efforts like Sanger Genomics of Drug Sensitivity in Cancer (GDSC) [15], Connectivity Map (CMAP) [16], the Cancer Therapeutics Response Portal (CTRP v1 and CTRP v2) [17, 18] further expanded the datasets. On the other hand, The Cancer Genome Atlas (TCGA) [19] and the International Cancer Genome Consortium (ICGC) [20] systematically characterised molecular profiles of thousands of tumours. These complementary data resources are valuable for understanding the complexity of cancer biology and connecting in vitro pharmacogenomic profiles to patient molecular characteristics, potentially informing anti-cancer treatment strategies.

Integrative analyses considering multiple data types of both cell lines and bulk tumours are still challenging and new analysis concepts tailored towards specific questions are an ongoing research topic. For instance, Cellector [21] preselects the most frequent genomic alterations and defines cancer subtypes based on a sequence of these alterations. Although such a preselection of genomic alterations integrates prior knowledge about cancer mutational patterns, it neglects complementary information contained in other data types. Furthermore, Cellector relies on a binary matrix of genomic alterations. This matrix is very sparse, since samples harbouring the same alterations are very rare. Therefore, the statistical power to detect appropriate cell lines for tumours might be limited.

A recent study [22] highlighted that independent classifiers based on different data types to predict cell line identity often yield inconsistent results. For example, predictions based on the mutation spectrum and oncogenic mutations can be contradictory, although both features are derived from mutation data. Complementary information from different data sources is integrated by the MAGNETIC-framework [23] into gene modules. Gene set enrichment analysis (GSEA) is then used to interpret these modules as pathways. MAGNETIC is indeed a powerful technique for integrating multiple molecular datasets and prior knowledge, but it does not conclude to what extent a cell line is suitable as a tumour model. The maui framework assigns cancer subtype labels to cell lines by extracting relevant features from multiple data types using a variational autoencoder (VAE) [24]. However, most of the maui embedded features are weakly associated with subtype labels and are therefore difficult to interpret.

Here, we propose MFmap, a new semi-supervised VAE architecture and objective function which combines good classification accuracy with good generative performance. We exploit these properties to derive subtype informed low dimensional representations for both cell lines and bulk tumours from high dimensional multi-omics data including gene expression, mutation and copy number variation. The latent representations can then be used to assess the similarity between a cell line and a tumour. We provide cell line by tumour dissimilarity matrices for CCLE and TCGA for the ten different cancer types listed in Table 1. In addition, MFmap predicts cancer subtype labels for cell lines. We demonstrate, how these predicted cancer subtypes can be used to transfer information from cell-line-based drug sensitivity screens to patient cohorts. We also show, that the latent representations learnt by MFmap are biologically interpretable. Finally, we illustrate how the generative nature of the MFmap model can be exploited for studying subtype transformations during cancer progression. At http://h2926513.stratoserver.net:3838/MFmap_shiny/ we provide a resource enabling researchers to select the most relevant cell line for a cancer patient.

Download:

Table 1. The sample size of TCGA and CCLE data used for training and testing MFmap.

https://doi.org/10.1371/journal.pone.0261183.t001

Materials and methods

Matching cell lines and tumours as a semi-supervised learning problem

MFmap is a semi-supervised deep neural network which integrates gene expression, copy number variation (CNV) and somatic mutation data with subtype classification. Each tumour sample t consists of a pair of (x_t, y_t), where denotes the high dimensional molecular features and y_t ∈ {1, …, h} is the cancer subtype label. For a cell line c, the cancer subtype is unknown and only the molecular features x_c are available. The index c or t will be suppressed, whenever we refer to a single observation. The MFmap neural network is trained in a semi-supervised manner using both cell line data and tumour data . Here, we used cell line data from CCLE and tumour data from TCGA.

One aim of MFmap is to use semi-supervised classification to infer the cancer subtype y_c of a cell line c. A second aim is to assess the similarity between a cell line and a tumour. Instead of comparing the high dimensional molecular features x_t and x_c directly, we first encode them into low dimensional latent representations z (see next section for details). Then, the similarity of a tumour sample t and a cell line c is measured as the cosine coefficient between the corresponding latent representation vectors z_t and z_c. We will also show that these latent representations z carry interpretable biological information.

The molecular data x = (x_DNA, x_RNA) consist of gene expression profiles x_RNA and network smoothed mutation and CNV profiles x_DNA. We will refer to these two parts as RNA and DNA view, respectively. The DNA view is obtained from the original binary mutation and CNV matrices (Fig 1(A)), which indicate the occurrence of a mutation or CNV event targeting a gene in a given tumour sample or cell line. These very sparse matrices are first projected onto an annotated cancer network [25]. By using a network diffusion algorithm [26], a mutation or CNV signal hitting a single gene is propagated to neighbouring nodes in the network, thereby enriching the mutation or CNV data by cancer network information. All molecular features were translated and scaled to the interval between zero and one.

Download:

Fig 1. Overview of MFmap.

(A) In a preprocessing step, mutation and CNV profiles are transformed to network smoothed DNA profiles. The original mutation and CNV data are represented as a binary matrix indicating the presence/absence of a DNA alteration in a given tumour sample or cell line. This sparse matrix is projected onto a cancer reference network (CRN) [25] and a network diffusion algorithm propagates this information to network neighbours, resulting in a dense DNA mutation or CNV matrix (DNA features). (B) The smoothed DNA features (DNA view) combined with gene expression data (RNA view) form the input of MFmap. The neural network architecture of MFmap has three components: encoder, decoder and classifier, encoded by different colours. The encoder maps sample features to a distribution q(z|x) for the latent representation z with mean value μ(x) and covariance σ²(x). The classifier outputs a molecular subtype probability p(y|z) and the decoder models a density p(x|z) for the reconstruction of the DNA and RNA views. During semi-supervised training, the molecular subtypes of tumour samples are used. (C) For visualisation, the latent representations of bulk tumour samples are used to generate a reference map. Cell lines are then projected to the reference map. The colour coding of individual samples or cell lines (dots) indicates the tumour subtype or the predicted subtype, respectively. The density of the tumour samples is indicated by background contour lines coloured according to the subtypes.

https://doi.org/10.1371/journal.pone.0261183.g001

Specification of MFmap as a semi-supervised generative model

The MFmap neural network (Fig 1(B)) is a new variant of a semi-supervised VAE [27]. The observable data are considered to be drawn from the probability distributions p(x, y) for tumour samples and p(x) for cell lines. These distributions are modelled as marginals over the latent variable , such that (1)

To facilitate biological interpretation of the latent representations, we set the dimension d of the latent space equal to the number of cancer subtypes h. In other applications of the MFmap model, one could also consider d as a tuneable hyper-parameter.

For the generative model, we assume x and y to be conditionally independent given the latent variable z. Accordingly, the joint distribution can be factorised as (2)

These distributions are specified as

Here, p(z) is the prior distribution for the latent representation vector. We denote the Gaussian distribution with mean vector μ and covariance matrix Σ by . The parameter π_θ(z) of the categorial distribution p(y|z) depends on the latent representation z. For the decoder p(x|z) one can chose a suitable distribution f_θ with parameters depending on the latent representations z [27]. The functions z ↦ π_θ(z) and z ↦ f_θ(·|z) are represented as neural networks. The parameters of these decoder networks are jointly denoted as θ.

For the mfMAP model we initially used a Gaussian distribution f_θ(x|z) to model the outputs. However, we found that rescaling the molecular features x to the interval [0, 1] and using a Bernoulli distribution for f_θ improved the semi-supervised classification accuracy (see Results section). Then, each single output of the decoder neural network z ↦ f_θ(·|z) can be interpreted as the probability, that the corresponding molecular feature is active or not. For instance, for the i−th component (x_RNA)_i of the RNA-view, the corresponding output can be regarded as the probability that the i-th gene is expressed.

Posterior inference, i.e. the evaluation of p(y, z|x) using Bayes theorem, is often intractable, because the marginal likelihood p(x) in Eq (1) requires integrating over z. Therefore, a variational distribution q(y, z|x) is introduced to approximate the true posterior [24, 27]. We assume that the variational distribution reflects the conditional independence x ⊥ y|z of the generative model in Eq (2). This implies (4)

For consistency we assume that q(y|z) in Eq (4) is identical to p(y|z) in Eq (3b) and is represented by the same neural network mapping z to the categorial parameter π_θ(z). For the variational distribution q(z|x) we choose a Gaussian (5) with parameters μ(x) and σ(x). The parameters are represented by the encoder neural network g_ϕ, which is itself parametrised by ϕ. The overall architecture of MFmap (Fig 1(B)) is thus formed by three neural networks, the encoder Eq (5), the classifier Eq (3b) and the decoder Eq (3c).

Training of MFmap using a semi-supervised loss function

Variational inference involves maximising an evidence lower bound (ELBO) to the log-likelihood of the observational data [24, 27]. For a single cell line sample one can derive a lower bound to the log-likelihood (6) which is identical to the ELBO of the basic VAE [24] for unsupervised learning (7) consisting of a reconstruction loss term and a Kullback-Leibler (KL) divergence term. For a single labelled tumour sample we have for the log-likelihood (8) where the ELBO for labelled examples reads (9)

To derive this ELBO (see S1 File), we exploited the conditional independence assumption x ⊥ y|z for both the generative model (Eq (2)) and the inference model (Eq (4)). The additional term in Eq (9) in comparison to Eq (7) can be interpreted as a classification loss. Given a tumour sample (x_t, y_t), the probability for the cancer subtype label p(y_t|z) is a function of z, which is inferred from q(z|x_t). This distribution is in turn determined by the molecular feature vector x_t.

We found empirically that the semi-supervised classification accuracy during training was relatively poor when using these exact negative ELBOs as loss functions. This is in line with previous findings that achieving both good semi-supervised classification accuracy and good generative performance is often difficult in VAEs [28] or other generative models [29]. Motivated by the work from [30], we added the negative entropy of the distribution p(y|z) to the unsupervised ELBO in Eq (7) and to the supervised ELBO in Eq (9). In summary, the MFmap loss functions for the unlabelled cell line and the labelled tumour data are respectively given by

This entropy regularisation encourages the classification boundaries to be located in low sample density regions [30] in the latent space, which improves the generalisation performance of the model. As shown below (see Results section), the semi-supervised classification accuracy was very convincing, when using this entropy regularisation.

During training, mini-batches b = 1, …, B from the cell line and tumour data are used to minimise (11) over different epochs. To check whether all terms in the MFmap loss function in Eq (10) can be jointly optimised, we recorded the values of each term in each training epoch and calculated their pair-wise correlations. The reconstruction loss -E_q(z|x)[p(x|z)], the KL-divergence D_KL(q(y|x)||p(z)), the entropy and the classification loss -E_q(z|x)[log p(x|z)] are highly correlated (Fig 2), what suggests that they are optimised simultaneously.

Download:

Fig 2. Joint optimisation of the reconstruction loss, the KL divergence, entropy and the classification loss with the MFmap loss function.

The plot shows the pairwise correlation of different terms in the MFmap loss function Eq (10) during different training epochs.

https://doi.org/10.1371/journal.pone.0261183.g002

Visualisation of individual samples

The MFmap latent representation z can be used to visualise and organise the associations of individual tumour samples and cell lines (Fig 1(C)). Inspired by the visualisation concept of Onco-GPS (OncoGenic Positioning System) [31], we used the tumour samples with known subtypes to generate a reference map for the cancer subtypes. In this reference map, the components z₁, …, z_h of the latent representation are presented as a graph with h corner points in a plane. The location of these corner points is determined by multidimensional scaling and is chosen so as to reflect the distances in the h-dimensional latent space as good as possible (see S1 File for details). An individual tumour sample can now be visualised as a point located in the area between the corner points. The location of such a point is given by a superposition of the corner positions weighted by the latent representation magnitudes of individual samples. In addition, the subtypes of the tumour samples are colour coded. The contour lines and the background colour shading represent the sample density in the region.

Once the reference map is established, individual cell lines can be projected to this map, where the colour of each dot encodes the subtype predicted by the MFmap classifier. This projection is based on the latent representation values of the cell line samples. Since our aim is to analyse the fidelity of a cell line as an oncological model for a given tumour or a cancer subtype, we name our framework the model fidelity map (MFmap).

Results

Evaluating the MFmap classification and generative performance

A direct evaluation of the MFmap subtype prediction for cell lines is impossible because there are no ground truth labels available. However, the classification accuracy on an unseen test dataset of bulk tumours provides an indirect evaluation of the subtype prediction performance. In Table 2 we used 20% of the tumour samples as independent test set and evaluated the classification performance using four multi-class classification metrics: overall accuracy, weighted precision, weighted recall, and weighted F₁ score. Similar results can be obtained, when 10% of the tumour samples are used for testing (see Table 1 in the S2 File). We also tested the effect of increasing the latent space dimension d and found that the classification accuracy was typically not higher, indicating that our choice of setting d equal to the number of cancer subtypes did not impair the classification accuracy (see Table 2 in the S2 File).

Download:

Table 2. MFmap subtype classification performance estimated for unseen tumour samples.

Here, 20% of the bulk tumour data were randomly selected as an independent test set.

https://doi.org/10.1371/journal.pone.0261183.t002

The good classification results for GBMLGG are intriguing, because the G-CIMP-High, G-CIMP-Low and LGm6-GBM subtypes were derived from methylation data [32], which were not used to train MFmap. This indicates that MFmap is able to extract DNA and RNA patterns reflecting features originally derived from different methylation status.

In addition, we tested how well the MFmap autoencoder part reconstructs the molecular features x. To this end, we first sampled a latent representations from the encoder q(z|x) for a given input x from the real data. Then, we correlated these original molecular features with the output sampled from the decoder distribution p(x|z). The histogram of Pearson correlation coefficients in Fig 3 shows a high input-output correlation for most molecular features for three exemplary cancer types: breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COADREAD) and glioblastoma multiforme and lower grade glioma (GBMLGG). Taken together, MFmap can combine very good classification accuracy with good generative performance.

Download:

Fig 3. The generative performance of MFmap.

The histogram shows sample-wise correlation coefficients between input features (DNA and RNA views) and reconstructed features output by the MFmap decoder.

https://doi.org/10.1371/journal.pone.0261183.g003

Future applications of MFmap will include the analysis of query samples input to a reference model trained on a large data set. To check how well MFmap can perform in such a setting, we checked various measures for the quality of integrating these data from different sources [33–35]. Since this is not the focus of this paper, we have relegated the very promising results to the Supporting Information (see S2 File).

Selecting the optimal cell line for a given tumour

The heatmaps in Fig 4 represent pairwise cell line by tumour dissimilarity matrices for three exemplary cancer types BRCA, COADREAD and GBMLGG. In addition, the subtypes of bulk tumours annotated from [32, 36, 37] and the subtypes of cell lines predicted by the MFmap classifier are displayed. For a better visualisation, cell lines and tumours are clustered based on their pairwise cosine dissimilarity scores. The similarity of a cell line c to a tumour t is defined as the cosine of the angle between their latent representations z_c and z_t. Accordingly, the dissimilarity between c and t is defined as . A dissimilarity of d(c, t) = 0 indicates perfect alignment between the latent representations of the cell line and the tumour, whereas a dissimilarity d(c, t) = 1 indicates orthogonal latent representations. The highest dissimilarity of d(c, t) = 2 would be achieved for antipodal latent vectors. Based on this dissimilarity matrix, researchers can select the best cell lines for a given tumour or a given tumour subtype. And, vice versa, the relevance of promising experimental results observed in vitro can be checked by selecting a subset of tumours most likely resembling the cell line characteristics. The pairwise dissimilarity matrices between TCGA bulk tumours and CCLE cell lines and cell line subtype predictions for all tumour types listed in Table 1 are provided on our website (http://h2926513.stratoserver.net:3838/MFmap_shiny/).

Download:

Fig 4. Pairwise dissimilarity between CCLE cell lines and TCGA bulk tumours.

The colour coding in the heatmaps indicates the pairwise dissimilarity which was obtained from the latent representations of cell lines and tumours for the three exemplary cancer types (A) breast invasive carcinoma (BRCA), (B) colorectal adenocarcinoma (COADREAD) and (C) glioblastoma multiforme and lower grade glioma (GBMLGG). Tumours (columns) and cell lines (rows) were clustered according to the dissimilarity score, which ranges from 0 (very similar) to 2 (very dissimilar). The subtype classification of each cell line was predicted from the classification layer of the MFmap neural network. The tables display the sample size for the different subtypes or predicted subtypes.

https://doi.org/10.1371/journal.pone.0261183.g004

These results also indicate, for which subtypes suitable cell line models exist and for which subtypes cell lines should be prioritised for future in vitro model development [21]. Each BRCA subtype is represented by at least three cell lines (Fig 4(A)) and the heatmap shows that these cell lines are very similar to the corresponding tumours of the same subtype. However, only three cell lines represents the HER2-enriched subtype. The four subtypes of COADREAD tumours are also well represented by at least six highly similar cell lines in CCLE (Fig 4(B)).

For GBMLGG, the Mesenchymal-like tumour subtype is represented by 31 cell lines with high similarity scores. Many TCGA tumour samples have the molecular subtype Codel and G-CIMP-high, but they are only represented by seven and nine cell lines, respectively. Only two cell lines were classified as Classic-like and a single cell line has the predicted subtype LGm6-GBM. The PA-like tumour subtype is not represented by any cell line.

Predicting drug sensitivity in cancer patient sub-cohorts using MFmap and in vitro drug screens

Predicting patient therapeutic response is one important goal of subtype stratification. To explore the translational potential of the subtypes predicted by MFmap we estimated the association between predicted subtypes and drug sensitivity of all compounds available in the CTRP dataset [18]. For each cancer type listed in Table 1 and each compound, we compared the drug sensitivity among different cell line subtypes predicted by the MFmap classifier. Drug sensitivity is quantified in CTRP by the area under the dose response curve (AUC). We used an ANOVA to test for differences in the mean AUC among the predicted subtypes. At a false discovery rate (FDR) cutoff of 25%, we found 18, six and 16 compounds in BRCA, GBMLGG and UCEC to show significant subtype specificity, respectively. For the other seven cancer types in Table 1, there are no significant AUC differences across the different subtypes. Note that the sample size per subtype is very small, which might explain why statistically significant results can only be obtained for three cancer types.

For BRCA, the compound with the strongest association between subtype and drug sensitivity is Lapatinib (ANOVA p-value = 2.95e-05). Lapatinib is a tyrosine kinase inhibitor used in combination therapy for HER2-positive breast cancer [38]. Our results suggest that cell lines of molecular subtype HER2-enriched are more sensitive to Lapatinib treatment (Fig 5(A)) in comparison to other three subtypes. Although there are only three cell lines representing the HER2-enriched subtype, this finding is in line with the known inhibitive mechanism of Lapatinib on the HER2/neu and epidermal growth factor receptor (EGFR) pathways. This result highlights the potential of MFmap as a tool for translating in vitro drug screening results to patient sub-cohorts. Our analysis also suggests that larger sample sizes and a better coverage of underrepresented subtypes are essential to increase the statistical power for detecting subtype specificity from cell line drug screens.

Download:

Fig 5. Cancer subtype specific drug sensitivity of CCLE cell lines.

The subtypes of breast invasive carcinoma (BRCA) cell lines respond differentially to the compounds Lapatinib and Olygomycin A. Treatment response to the compounds KHS101 and Bortezomib in of glioblastoma multiforme and lower grade glioma (GBMLGG) cell lines is subtype specific. The drug sensitivity is summarised by the area under the dose response curve (AUC) and p-values refer to an ANOVA of the AUC differences among different subtypes.

https://doi.org/10.1371/journal.pone.0261183.g005

Another drug with significant variations of the AUC values across the different BRCA subtypes is Oligomycin A (ANOVA p-value = 1.39e-4), a compound targeting oxidative phosphorylation via an inhibition of the ATP synthase. The potential of Oligomycin A as a therapeutic compound to prevent metastatic spread in breast cancer has recently been highlighted [39]. The results in Fig 5(B) suggest that treatment with Oligomycin A might be most efficient for the HER2-enriched and Luminal A or Luminal B subtypes.

The drug sensitivities of KHS101 and Bortezomib are significantly associated with GBMLGG subtypes (KHS101: ANOVA p-value = 2.3e-04; Bortezomib: ANOVA p-value = 2.3e-04). The synthetic small molecule KHS101 was shown to promote tumour cell death in diverse glioblastoma multiforme cell line models [40]. Our analysis suggests that the G-CIMP-low subtype is more sensitive to KSH101 treatment (Fig 5(C)) compared to the other six GBMLGG subtypes. G-CIMP-low is an IDH mutant glioma subtype with poor clinical outcome in recurrent glioma [32].

Bortezomib targets the ubiquitin-proteasome pathway and is used for the treatment of multiple myeloma, but has also been discussed as treatment for glioma [41]. Our results in Fig 5(D) show that the Codel and G-CIMP-high subtypes have larger AUCs. The results for LGm6-GBM and Classic-like are not conclusive because there are not enough cell lines representing these subtypes.

Biological characterisation of latent representations learnt by MFmap

The pattern of MFmap learnt latent representations z can be used as a signature for cancer subtypes. For example, in BRCA, the basal-like subtype is characterised by a pattern of low values of components z₁ and z₄ and high values of z₂ and z₃ (Fig 6(A)). HER2-enriched tumours are characterised by high values of z₁ and z₃ and z₄. Luminal A and B subtypes can be distinguished by z₄. Similarly, cancer subtypes in COADREAD and GBMLGG are highly associated with their latent representations learnt by MFmap (Fig 6(B) and 6(C)).

Download:

Fig 6. Association of MFmap latent representations and cancer subtypes.

The dimension of the latent representation h is set to the number of cancer subtypes. The boxplots display latent representations of different subtypes of TCGA samples in the three exemplary cancer types (A) breast invasive carcinoma (BRCA), (B) colorectal adenocarcinoma (COADREAD) and (C) glioblastoma multiforme and lower grade glioma (GBMLGG). Cancer subtypes are colour encoded and sorted by their median latent representations.

https://doi.org/10.1371/journal.pone.0261183.g006

To further investigate the biological meaning of the latent representations we analysed the association between z and pathway activities in TCGA reference datasets. We used single sample gene set enrichment analysis (ssGSEA) [42] to assess sample-wise pathway activities. The pathway signatures were compiled from several sources including 10 curated oncogenic signalling pathways [43], 19 curated specific DNA damage repair (DDR) pathways [44], 14 expert-curated specific DDR processes and DDR associated processes [45]. This collection was combined with MsigDB (v7.0) [46] chemical and genetic perturbations (CGP) and canonical pathways (CP) collections (MsigDB C2 collection) and MsigDB (v7.0) hallmark gene sets (MsigDB H collection). The degree of associations was quantified by the information coefficient and the Pearson correlation coefficient and the statistical significance was assessed by permutation tests. To tackle class imbalance in the different subtypes, we applied SMOTE upsampling [47].

We used COADREAD as a proof of concept, because it has four well characterised molecular subtypes CMS1-CMS4 [37]. The CMS1 subtype is characterised by micro-satellite instability (MSI), whereas CMS4 tumours are micro-satellite stable. The CMS4 subtype is also distinguished from CMS1 by epithelial mesenchymal transformation (EMT) characteristics, accompanied by prominent stromal invasion and angiogenesis. These mutually exclusive characteristics are clearly reflected in the magnitudes of the latent representation components. The top gene sets associated with component z₂ are “WATANABE COLON CANCER MSI VS MSS UP” and “KOINUMA COLON CANCER MSI UP”, whereas z₄ is associated with the activity of gene sets annotated as “HALLMARK ANGIOGENESIS” and “HALLMARK EPITHELIAL MESENCHYMAL TRANSITION”. Clearly, high values of z₂ are a characteristics of the CMS1 subtype, whereas high values of z₄ are a distinctive feature of CMS4 tumours. This example illustrates that a meaningful way to guide biological interpretation of the latent representations is to associate them to single sample pathway activity.

The same method was applied to annotate latent representations of GBMLGG (Fig 7(A)), which has seven subtypes [32]. The Mesenchymal-like and PA-like are stratified by gene expression profiles and the G-CIMP-high, G-CIMP-low and LGm6-GBM are methylation based. The Codel subtype describes IDH-mutant samples harbouring a co-deletion of chromosome arm 1p and 19q. Many pathways associated with latent representation z₁ are related to the neurotransmitter release cycle, which is also a characteristics of the Verhaak proneuronal subtype [48]. Pathways correlated to latent representation z₂ are related to the mesenchymal cell type, hypoxia and angiogenesis, which characterises the Verhaak mesenchymal subtype. The activity of the Fanconi Anemia (FA) DNA repair pathway is highly correlated with latent representation z₃. DNA damage response deficiency and amplified oncogenic MYC signalling characterises tumours with large values of latent representation z₄. Latent representation z₅ is related to the neurotransmitter release cycle and dysfunctional metabolism; latent representation z₆ to mitotic checkpoint deficiency. Many pathways associated with latent representation z₇ are involved in mismatch repair deficiency, replication stress and cell cycle disregulation and also related to the classical subtype in the earlier classification of Verhaak [48].

Download:

Fig 7. Characterising the MFmap learnt latent representations in glioblastoma multiforme and lower grade glioma (GBMLGG).

(A) The top heatmap shows the latent representation z of TCGA tumour samples (columns). The tumour samples are ordered based on a hierarchical clustering of z and their subtypes are colour encoded. The heatmap at the bottom displays sample-wise pathway activities that are significantly associated with the latent representations z₁, …, z₇. Pathway activities were computed using the ssGSEA algorithm [42]. For better visualisation, we upsampled the input data of MFmap and ssGSEA to get a balanced sample size in each subtype. (B) The MFmap reference map is formed by projecting the latent representations z of bulk tumours into two dimensions using multidimensional scaling. It consists of seven dominant components represented by black nodes. The length of their connections is given by the Euclidean distance of the dominant components in the latent space. The annotation of the seven dominant nodes is based on the correlation between z and pathway activity scores (see A). The background colour encodes sample subtypes, and the background contour encodes sample density. Individual bulk tumours are displayed as dots on the MFmap reference map. (C) Cell line samples are projected to the MFmap reference map. In both (B) and (C), the subtype of bulk tumours and predicted subtype of cell lines are colour coded. Subtype specific sample size for bulk tumours and cell lines is reported in the legend table.

https://doi.org/10.1371/journal.pone.0261183.g007

Individual samples and their relationships can be displayed in the MFmap reference map (Fig 7(B)), a visualisation tool adapted from OncoGPS [31]. Here, the seven corners of the map correspond to the respective latent representations z₁, …, z₇ in GBMLGG. The corner locations are determined by multidimensional scaling on the latent representations of bulk tumours. Individual bulk tumour samples are displayed as dots in the regions between the corner points with locations determined by a weighted vector sum of the seven corner locations (see S1 File for details). The subtype of each tumour sample is indicated by colours. The density of the tumour samples of a given subtype is depicted by the contour lines and the corresponding colour shading. Fig 7(B)) shows that samples of the same subtype clustered together and the inter-cluster distance is large. Projecting cell lines to the MFmap reference map (Fig 7(C)) helps to visualise the relationship between their predicted subtypes and their latent representations.

Modelling cellular state transformations using latent space arithmetics

Cancerous neoplasms undergo various biochemical changes during cancer evolution and in response to selective pressure. One example is the transition from a proneural to a mesenchymal phenotype in glioblastoma, which is characterised by acquired therapeutic resistance and more aggressive potential [49]. In the DNA methylation based subtype classification of [32], the G-CIMP-high methylation phenotype tends to have the proneural molecular subtype [48] (see Fig 7(B)). Given that the latent representations learnt by MFmap clearly distinguish these different subtypes, we asked, whether the generative nature of the semi-supervised VAE can also be exploited to study such cancer subtype transformations.

To this end, we used the latent representations of the G-CIMP-high tumours and the Mesenchymal-like tumours (see Fig 7(B)) and computed the centroid vectors and for the corresponding tumour samples. The difference was used as a latent perturbation vector. By adding δ to the latent representation of each G-CIMP-high tumour (Fig 8(A)) we obtained the latent representation of in silico samples (Fig 8(B)), which are located in the “Mesenchymal-like region” of the reference map. We used these latent representation vectors of the in silico samples as input to the decoder of the MFmap network. We then checked, whether key molecular features of real Mesenchymal-like samples are reflected by these generated samples. Based on the available biological knowledge, we focussed on the most prominent onco-markers of the G-CIMP-high subtype: mutation status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX), isocitrate dehydrogenase (IDH) and TP53 genes. The original G-CIMP-high tumours show a high propensity towards mutations in these genes, indicated by relatively higher network smoothed mutation scores (Fig 8(C)), although not all samples are necessarily harbouring these mutations. In contrast, the predicted mutation scores for the perturbed in silico samples in Fig 8(B) are much lower, indicating a lower propensity to IDH1, ATRX or TP53 mutations. This is in agreement with the observed tendency of Mesenchymal-like tumours for these mutations [49]. This example not only highlights the good generative performance of MFmap but also hints at potential applications on integrative analysis of cancer evolution dynamics.

Download:

Fig 8. In-silico perturbation analysis of cellular state changes during disease transformation from the G-CIMP-high to the Mesenchymal-like subtype in glioblastoma multiforme and lower grade glioma (GBMLGG).

(A) The G-CIMP-high tumours from TCGA are projected to the MFmap reference map. (B) By perturbing the latent representation vectors of these G-CIMP-high tumours we generate artificial tumour samples located in the Mesenchymal-like region of the MFmap reference map (compare Fig 7(B)). (C) Boxplots of the sample mutation status (network smoothed mutation scores) of marker genes IDH1, ATRX1 and TP53 before and after perturbation.

https://doi.org/10.1371/journal.pone.0261183.g008

Discussion

Limited success in translating in vitro therapeutic markers to clinical applications highlights that not all cell lines are good models for a given cancer subtype. Selecting the most appropriate cell line for a given tumour or a set of tumours is crucial for understanding cancer biology and developing new anti-cancer treatments. Here, we provide a computational framework and a resource for cancer researchers to select the best cell lines for a TCGA tumour or a cancer subtype from ten different cancer types (http://h2926513.stratoserver.net:3838/MFmap_shiny/). The quantitative similarity score enables researchers to judge, whether a given tumour or a subtype of tumours is well represented by a cell line.

The assignment of cancer subtype labels to cell lines enables cell biologists to optimise experimental planning and to focus their research on clinically relevant model systems. We found that our semi-supervised MFmap model can classify tumours with a very high accuracy. Further analysis of drug sensitivity profiles supports that the subtype prediction for cell lines is biologically meaningful. Our analysis shows that HER2-enriched cell lines are most sensitive to Lapatinib, in agreement with prior knowledge about drug efficiency of this compound. As an example for the translation of in vitro pharmacogenomic data, we predict that the G-CIMP-low subtype is more sensitive to the new synthetic compound KHS101 compared to other GBMLGG subtypes.

Our finding that only BRCA, GBMLGG and UCEC show significant subtype specific drug sensitivity variation merits further investigation. One important reason is the small number of cell lines representing some cancer subtypes, which prevents us from finding statistically significant variations of drug sensitivity across the different subtypes. This highlights the need to prioritise cell line development for underrepresented disease variants [21]. However, it can not be ruled out that for some cancers the known subtype classifications are not predictive of drug sensitivity. This suggests that clinically relevant subtype stratification should take into account drug sensitivity.

By embedding the original gene expression space, somatic mutation space and copy number space of bulk tumours and cell lines into a lower dimensional latent space, MFmap extracts latent features that are strongly associated with cancer subtypes. For COADREAD and GLMBGG, we have illustrated that the abstract latent representations can be annotated biologically using their associations with pathway activities. This makes the latent representations interpretable and allows to study the molecular and clinical heterogeneity of this disease. In principle, MFmap can be complemented by other modalities such as methylation or proteomics data. However, for our purpose we found that gene expression and DNA features in combination with the prior knowledge about tumour subtypes contains sufficient information.

Our proof of principle analysis of the transformation between two different tumour subtypes presents a new approach for studying tumour evolutionary processes in a more integrative way [50]. The small sample size of some multi-region sequencing or single-cell sequencing studies limits the ability to infer robust evolutionary patterns. By projecting these data to the MFmap reference map obtained from training on large sets of bulk tumour data one could deduce useful phenotypic information for individual patients. We believe that this can leverage information gathered in large cancer genomic studies like TCGA to guide personalised clinical decision making.

The MFmap is based on a new semi-supervised neural network architecture combining a basic VAE with an additional classifier. Such semi-supervised learning tasks are very common in the biomedical research field, because it is often easier to acquire a large number of measurements than to obtain the corresponding labels. Based on the good predictive and generative performance of MFmap together with the evidence provided here, that MFmap can learn biologically and clinically meaningful information, we are convinced that the MFmap model can be adapted to other semi-supervised tasks in oncology and beyond.

Supporting information

S1 File. Extended method details.

https://doi.org/10.1371/journal.pone.0261183.s001

(PDF)

S2 File. Further evaluation of the MFmap performance.

https://doi.org/10.1371/journal.pone.0261183.s002

(PDF)

S1 Data.

https://doi.org/10.1371/journal.pone.0261183.s003

(TXT)

References

1. Sharma SV, Haber DA, Settleman J. Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents. Nature Reviews Cancer. 2010;10(4):241–253. pmid:20300105
- View Article
- PubMed/NCBI
- Google Scholar
2. Kim N, He N, Yoon S. Cell line modeling for systems medicine in cancers (Review). International Journal of Oncology. 2014;44(2):371–376. pmid:24297677
- View Article
- PubMed/NCBI
- Google Scholar
3. Goodspeed A, Heiser LM, Gray JW, Costello JC. Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics. Molecular Cancer Research. 2016;14(1):3–13. pmid:26248648
- View Article
- PubMed/NCBI
- Google Scholar
4. Kaur G, Dufour JM. Cell lines: Valuable tools or useless artifacts. Spermatogenesis. 2012;2(1):1–5. pmid:22553484
- View Article
- PubMed/NCBI
- Google Scholar
5. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–607.
- View Article
- Google Scholar
6. Mouradov D, Sloggett C, Jorissen RN, Love CG, Li S, Burgess AW, et al. Colorectal Cancer Cell Lines Are Representative Models of the Main Molecular Subtypes of Primary Cancer. Cancer Research. 2014;74(12):3238–3247. pmid:24755471
- View Article
- PubMed/NCBI
- Google Scholar
7. Ince TA, Sousa AD, Jones MA, Harrell JC, Agoston ES, Krohn M, et al. Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours. Nature Communications. 2015;6(1):7419. pmid:26080861
- View Article
- PubMed/NCBI
- Google Scholar
8. Cheng H, Yang X, Si H, Saleh AD, Xiao W, Coupar J, et al. Genomic and Transcriptomic Characterization Links Cell Lines with Aggressive Head and Neck Cancers. Cell Reports. 2018;25(5):1332–1345.e5. pmid:30380422
- View Article
- PubMed/NCBI
- Google Scholar
9. Domcke S, Sinha R, Levine DA, Sander C, Schultz N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nature Communications. 2013;4(1):2126. pmid:23839242
- View Article
- PubMed/NCBI
- Google Scholar
10. Liu K, Newbury PA, Glicksberg BS, Zeng WZD, Paithankar S, Andrechek ER, et al. Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data. Nature Communications. 2019;10(1):2138. pmid:31092827
- View Article
- PubMed/NCBI
- Google Scholar
11. Sinha R, Winer AG, Chevinsky M, Jakubowski C, Chen YB, Dong Y, et al. Analysis of renal cancer cell lines from two major resources enables genomics-guided cell line selection. Nature Communications. 2017;8(1):15165. pmid:28489074
- View Article
- PubMed/NCBI
- Google Scholar
12. Yu K, Chen B, Aran D, Charalel J, Yau C, Wolf DM, et al. Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types. Nature Communications. 2019;10(1):3574. pmid:31395879
- View Article
- PubMed/NCBI
- Google Scholar
13. Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen. Nature Reviews Cancer. 2006;6(10):813–823. pmid:16990858
- View Article
- PubMed/NCBI
- Google Scholar
14. Ghandi M, Huang FW, Jané-Valbuena J, Kryukov GV, Lo CC, McDonald ER, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019;569(7757):503–508. pmid:31068700
- View Article
- PubMed/NCBI
- Google Scholar
15. Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740–754. pmid:27397505
- View Article
- PubMed/NCBI
- Google Scholar
16. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science. 2006;313(5795):1929. pmid:17008526
- View Article
- PubMed/NCBI
- Google Scholar
17. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, et al. An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules. Cell. 2013;154(5):1151–1161. pmid:23993102
- View Article
- PubMed/NCBI
- Google Scholar
18. Seashore-Ludlow B, Rees MG, Cheah JH, Cokol M, Price EV, Coletti ME, et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discovery. 2015;5(11):1210. pmid:26482930
- View Article
- PubMed/NCBI
- Google Scholar
19. Chang K, Creighton CJ, Davis C, Donehower L, Drummond J, Wheeler D, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genetics. 2013;45(10):1113–1120.
- View Article
- Google Scholar
20. Hudson (Chairperson) TJ, Anderson W, Aretz A, Barker AD, Bell C, Bernabé RR, et al. International network of cancer genome projects. Nature. 2010;464(7291):993–998.
- View Article
- Google Scholar
21. Najgebauer H, Yang M, Francies HE, Pacini C, Stronach EA, Garnett MJ, et al. CELLector: Genomics-Guided Selection of Cancer In Vitro Models. Cell Systems. 2020;10(5):424–432.e6. pmid:32437684
- View Article
- PubMed/NCBI
- Google Scholar
22. Salvadores M, Fuster-Tormo F, Supek F. Matching cell lines with cancer type and subtype of origin via mutational, epigenomic, and transcriptomic patterns. Science Advances. 2020;6(27):eaba1862. pmid:32937430
- View Article
- PubMed/NCBI
- Google Scholar
23. Webber JT, Kaushik S, Bandyopadhyay S. Integration of Tumor Genomic Data with Cell Lines Using Multi-dimensional Network Modules Improves Cancer Pharmacogenomics. Cell Systems. 2018;7(5):526–536.e6. pmid:30414925
- View Article
- PubMed/NCBI
- Google Scholar
24. Kingma DP, Welling M. Auto-Encoding Variational Bayes. arXiv:13126114 [Preprint]. 2013;Available from: https://arxiv.org/pdf/1312.6114.pdf.
25. Huang JK, Jia T, Carlin DE, Ideker T. pyNBS: a Python implementation for network-based stratification of tumor mutations. Bioinformatics. 2018;34(16):2859–2861. pmid:29608663
- View Article
- PubMed/NCBI
- Google Scholar
26. Hofree M, Shen JP, Carter H, Gross A, Ideker T. Network-based stratification of tumor mutations. Nature Methods. 2013;10(11):1108–1115. pmid:24037242
- View Article
- PubMed/NCBI
- Google Scholar
27. Kingma DP, Rezende DJ, Mohamed S, Welling M. Semi-Supervised Learning with Deep Generative Models. arXiv:14065298v2[Preprint]. 2014; Available from:https://arxiv.org/pdf/1406.5298.pdf.
28. Feng H, Kong K, Chen M, Zhang T, Zhu M, Chen W. SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations. arXiv:201110684[Preprint]. 2020;abs/2011.10684. Available from:https://arxiv.org/pdf/2011.10684.pdf.
29. Dai Z, Yang Z, Yang F, Cohen WW, Salakhutdinov R. Good Semi-supervised Learning that Requires a Bad GAN. arXiv:170509783[Preprint]. 2017; Available from:https://arxiv.org/pdf/1705.09783.pdf.
30. Grandvalet Y, Bengio Y. Semi-Supervised Learning by Entropy Minimization. In: Proceedings of the 17th International Conference on Neural Information Processing Systems. NIPS’04. Cambridge, MA, USA: MIT Press; 2004. p. 529–536.
31. Kim JW, Abudayyeh OO, Yeerna H, Yeang CH, Stewart M, Jenkins RW, et al. Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States. Cell Systems. 2017;5(2):105–118.e9. pmid:28837809
- View Article
- PubMed/NCBI
- Google Scholar
32. Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA, et al. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell. 2016;164(3):550–563. pmid:26824661
- View Article
- PubMed/NCBI
- Google Scholar
33. Lotfollahi M, Naghipourfar M, Luecken MD, Khajavi M, Büttner M, Wagenstetter M, et al. Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology. 2021. pmid:34462589
- View Article
- PubMed/NCBI
- Google Scholar
34. Stein-O’Brien GL, Clark BS, Sherman T, Zibetti C, Hu Q, Sealfon R, et al. Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species. Cell Systems. 2019;8(5):395–411.e8. pmid:31121116
- View Article
- PubMed/NCBI
- Google Scholar
35. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck I William M, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888–1902.e21. pmid:31178118
- View Article
- PubMed/NCBI
- Google Scholar
36. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–352. pmid:22522925
- View Article
- PubMed/NCBI
- Google Scholar
37. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nature Medicine. 2015;21(11):1350–1356. pmid:26457759
- View Article
- PubMed/NCBI
- Google Scholar
38. Higa GM, Abraham J. Lapatinib in the treatment of breast cancer. Expert Review of Anticancer Therapy. 2007;7(9):1183–1192. pmid:17892419
- View Article
- PubMed/NCBI
- Google Scholar
39. Davis RT, Blake K, Ma D, Gabra MBI, Hernandez GA, Phung AT, et al. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat Cell Biol. 2020;22(3):310–320. pmid:32144411
- View Article
- PubMed/NCBI
- Google Scholar
40. Polson ES, Kuchler VB, Abbosh C, Ross EM, Mathew RK, Beard HA, et al. KHS101 disrupts energy metabolism in human glioblastoma cells and reduces tumor growth in mice. Science Translational Medicine. 2018;10(454):eaar2718. pmid:30111643
- View Article
- PubMed/NCBI
- Google Scholar
41. Tang JH, Yang L, Chen JX, Li QR, Zhu LR, Xu QF, et al. Bortezomib inhibits growth and sensitizes glioma to temozolomide (TMZ) via down-regulating the FOXM1–Survivin axis. Cancer Commun. 2019;39(1):81. pmid:31796105
- View Article
- PubMed/NCBI
- Google Scholar
42. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14(1):7. pmid:23323831
- View Article
- PubMed/NCBI
- Google Scholar
43. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell. 2018;173(2):321–337.e10. pmid:29625050
- View Article
- PubMed/NCBI
- Google Scholar
44. Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, et al. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Reports. 2018;23(1):239–254.e6. pmid:29617664
- View Article
- PubMed/NCBI
- Google Scholar
45. Pearl LH, Schierz AC, Ward SE, Al-Lazikani B, Pearl FMG. Therapeutic opportunities within the DNA damage response. Nature Reviews Cancer. 2015;15(3):166–180. pmid:25709118
- View Article
- PubMed/NCBI
- Google Scholar
46. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–1740. pmid:21546393
- View Article
- PubMed/NCBI
- Google Scholar
47. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research. 2002;16:321–357.
- View Article
- Google Scholar
48. Verhaak RGW, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, et al. Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17(1):98–110. pmid:20129251
- View Article
- PubMed/NCBI
- Google Scholar
49. Behnan J, Finocchiaro G, Hanna G. The landscape of the mesenchymal signature in brain tumours. Brain. 2019;142(4):847–866. pmid:30946477
- View Article
- PubMed/NCBI
- Google Scholar
50. Williams MJ, Sottoriva A, Graham TA. Measuring Clonal Evolution in Cancer with Genomics. Annu Rev Genom Hum Genet. 2019;20(1):309–329. pmid:31059289
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Sharma SV, Haber DA, Settleman J. Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents. Nature Reviews Cancer. 2010;10(4):241–253. pmid:20300105
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Kim N, He N, Yoon S. Cell line modeling for systems medicine in cancers (Review). International Journal of Oncology. 2014;44(2):371–376. pmid:24297677
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Goodspeed A, Heiser LM, Gray JW, Costello JC. Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics. Molecular Cancer Research. 2016;14(1):3–13. pmid:26248648
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Kaur G, Dufour JM. Cell lines: Valuable tools or useless artifacts. Spermatogenesis. 2012;2(1):1–5. pmid:22553484
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–607.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref6] 6. Mouradov D, Sloggett C, Jorissen RN, Love CG, Li S, Burgess AW, et al. Colorectal Cancer Cell Lines Are Representative Models of the Main Molecular Subtypes of Primary Cancer. Cancer Research. 2014;74(12):3238–3247. pmid:24755471
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Ince TA, Sousa AD, Jones MA, Harrell JC, Agoston ES, Krohn M, et al. Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours. Nature Communications. 2015;6(1):7419. pmid:26080861
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref8] 8. Cheng H, Yang X, Si H, Saleh AD, Xiao W, Coupar J, et al. Genomic and Transcriptomic Characterization Links Cell Lines with Aggressive Head and Neck Cancers. Cell Reports. 2018;25(5):1332–1345.e5. pmid:30380422
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref9] 9. Domcke S, Sinha R, Levine DA, Sander C, Schultz N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nature Communications. 2013;4(1):2126. pmid:23839242
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Liu K, Newbury PA, Glicksberg BS, Zeng WZD, Paithankar S, Andrechek ER, et al. Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data. Nature Communications. 2019;10(1):2138. pmid:31092827
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Sinha R, Winer AG, Chevinsky M, Jakubowski C, Chen YB, Dong Y, et al. Analysis of renal cancer cell lines from two major resources enables genomics-guided cell line selection. Nature Communications. 2017;8(1):15165. pmid:28489074
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Yu K, Chen B, Aran D, Charalel J, Yau C, Wolf DM, et al. Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types. Nature Communications. 2019;10(1):3574. pmid:31395879
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen. Nature Reviews Cancer. 2006;6(10):813–823. pmid:16990858
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref14] 14. Ghandi M, Huang FW, Jané-Valbuena J, Kryukov GV, Lo CC, McDonald ER, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019;569(7757):503–508. pmid:31068700
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref15] 15. Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740–754. pmid:27397505
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref16] 16. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science. 2006;313(5795):1929. pmid:17008526
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref17] 17. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, et al. An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules. Cell. 2013;154(5):1151–1161. pmid:23993102
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref18] 18. Seashore-Ludlow B, Rees MG, Cheah JH, Cokol M, Price EV, Coletti ME, et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discovery. 2015;5(11):1210. pmid:26482930
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref19] 19. Chang K, Creighton CJ, Davis C, Donehower L, Drummond J, Wheeler D, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genetics. 2013;45(10):1113–1120.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref20] 20. Hudson (Chairperson) TJ, Anderson W, Aretz A, Barker AD, Bell C, Bernabé RR, et al. International network of cancer genome projects. Nature. 2010;464(7291):993–998.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref21] 21. Najgebauer H, Yang M, Francies HE, Pacini C, Stronach EA, Garnett MJ, et al. CELLector: Genomics-Guided Selection of Cancer In Vitro Models. Cell Systems. 2020;10(5):424–432.e6. pmid:32437684
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref22] 22. Salvadores M, Fuster-Tormo F, Supek F. Matching cell lines with cancer type and subtype of origin via mutational, epigenomic, and transcriptomic patterns. Science Advances. 2020;6(27):eaba1862. pmid:32937430
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref23] 23. Webber JT, Kaushik S, Bandyopadhyay S. Integration of Tumor Genomic Data with Cell Lines Using Multi-dimensional Network Modules Improves Cancer Pharmacogenomics. Cell Systems. 2018;7(5):526–536.e6. pmid:30414925
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref24] 24. Kingma DP, Welling M. Auto-Encoding Variational Bayes. arXiv:13126114 [Preprint]. 2013;Available from: https://arxiv.org/pdf/1312.6114.pdf.

[ref25] 25. Huang JK, Jia T, Carlin DE, Ideker T. pyNBS: a Python implementation for network-based stratification of tumor mutations. Bioinformatics. 2018;34(16):2859–2861. pmid:29608663
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref26] 26. Hofree M, Shen JP, Carter H, Gross A, Ideker T. Network-based stratification of tumor mutations. Nature Methods. 2013;10(11):1108–1115. pmid:24037242
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref27] 27. Kingma DP, Rezende DJ, Mohamed S, Welling M. Semi-Supervised Learning with Deep Generative Models. arXiv:14065298v2[Preprint]. 2014; Available from:https://arxiv.org/pdf/1406.5298.pdf.

[ref28] 28. Feng H, Kong K, Chen M, Zhang T, Zhu M, Chen W. SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations. arXiv:201110684[Preprint]. 2020;abs/2011.10684. Available from:https://arxiv.org/pdf/2011.10684.pdf.

[ref29] 29. Dai Z, Yang Z, Yang F, Cohen WW, Salakhutdinov R. Good Semi-supervised Learning that Requires a Bad GAN. arXiv:170509783[Preprint]. 2017; Available from:https://arxiv.org/pdf/1705.09783.pdf.

[ref30] 30. Grandvalet Y, Bengio Y. Semi-Supervised Learning by Entropy Minimization. In: Proceedings of the 17th International Conference on Neural Information Processing Systems. NIPS’04. Cambridge, MA, USA: MIT Press; 2004. p. 529–536.

[ref31] 31. Kim JW, Abudayyeh OO, Yeerna H, Yeang CH, Stewart M, Jenkins RW, et al. Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States. Cell Systems. 2017;5(2):105–118.e9. pmid:28837809
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref32] 32. Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA, et al. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell. 2016;164(3):550–563. pmid:26824661
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref33] 33. Lotfollahi M, Naghipourfar M, Luecken MD, Khajavi M, Büttner M, Wagenstetter M, et al. Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology. 2021. pmid:34462589
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref34] 34. Stein-O’Brien GL, Clark BS, Sherman T, Zibetti C, Hu Q, Sealfon R, et al. Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species. Cell Systems. 2019;8(5):395–411.e8. pmid:31121116
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref35] 35. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck I William M, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888–1902.e21. pmid:31178118
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref36] 36. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–352. pmid:22522925
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref37] 37. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nature Medicine. 2015;21(11):1350–1356. pmid:26457759
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref38] 38. Higa GM, Abraham J. Lapatinib in the treatment of breast cancer. Expert Review of Anticancer Therapy. 2007;7(9):1183–1192. pmid:17892419
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref39] 39. Davis RT, Blake K, Ma D, Gabra MBI, Hernandez GA, Phung AT, et al. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat Cell Biol. 2020;22(3):310–320. pmid:32144411
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref40] 40. Polson ES, Kuchler VB, Abbosh C, Ross EM, Mathew RK, Beard HA, et al. KHS101 disrupts energy metabolism in human glioblastoma cells and reduces tumor growth in mice. Science Translational Medicine. 2018;10(454):eaar2718. pmid:30111643
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref41] 41. Tang JH, Yang L, Chen JX, Li QR, Zhu LR, Xu QF, et al. Bortezomib inhibits growth and sensitizes glioma to temozolomide (TMZ) via down-regulating the FOXM1–Survivin axis. Cancer Commun. 2019;39(1):81. pmid:31796105
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref42] 42. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14(1):7. pmid:23323831
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref43] 43. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell. 2018;173(2):321–337.e10. pmid:29625050
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref44] 44. Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, et al. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Reports. 2018;23(1):239–254.e6. pmid:29617664
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref45] 45. Pearl LH, Schierz AC, Ward SE, Al-Lazikani B, Pearl FMG. Therapeutic opportunities within the DNA damage response. Nature Reviews Cancer. 2015;15(3):166–180. pmid:25709118
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref46] 46. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–1740. pmid:21546393
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref47] 47. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research. 2002;16:321–357.
View Article
Google Scholar

[168] View Article

[169] Google Scholar

[ref48] 48. Verhaak RGW, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, et al. Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17(1):98–110. pmid:20129251
View Article
PubMed/NCBI
Google Scholar

[171] View Article

[172] PubMed/NCBI

[173] Google Scholar

[ref49] 49. Behnan J, Finocchiaro G, Hanna G. The landscape of the mesenchymal signature in brain tumours. Brain. 2019;142(4):847–866. pmid:30946477
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref50] 50. Williams MJ, Sottoriva A, Graham TA. Measuring Clonal Evolution in Cancer with Genomics. Annu Rev Genom Hum Genet. 2019;20(1):309–329. pmid:31059289
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Matching cell lines and tumours as a semi-supervised learning problem

Specification of MFmap as a semi-supervised generative model

Training of MFmap using a semi-supervised loss function

Visualisation of individual samples

Results

Evaluating the MFmap classification and generative performance

Selecting the optimal cell line for a given tumour

Predicting drug sensitivity in cancer patient sub-cohorts using MFmap and in vitro drug screens

Biological characterisation of latent representations learnt by MFmap

Modelling cellular state transformations using latent space arithmetics

Discussion

Supporting information

S1 File. Extended method details.

S2 File. Further evaluation of the MFmap performance.

S1 Data.

References