Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

ATR suppresses endogenous DNA damage and allows completion of homologous recombination repair.

DNA replication fork stalling or collapse that arises from endogenous damage poses a serious threat to genome stability, but cells invoke an intricate signaling cascade referred to as the DNA damage response (DDR) to prevent such damage. The gene product ataxia telangiectasia and Rad3-related (ATR) responds primarily to replication stress by regulating cell cycle checkpoint control, yet it's role in DNA repair, particularly homologous recombination (HR), remains unclear. This is of particular interest since HR is one way in which replication restart can occur in the presence of a stalled or collapsed fork. Hypomorphic mutations in human ATR cause the rare autosomal-recessive disease Seckel syndrome, and complete loss of Atr in mice leads to embryonic lethality. We recently adapted the in vivo murine pink-eyed unstable (pun) assay for measuring HR frequency to be able to investigate the role of essential genes on HR using a conditional Cre/loxP system. Our system allows for the unique opportunity to test the effect of ATR loss on HR in somatic cells under physiological conditions. Using this system, we provide evidence that retinal pigment epithelium (RPE) cells lacking ATR have decreased density with abnormal morphology, a decreased frequency of HR and an increased level of chromosomal damage

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

Abstract Background The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. Results We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Conclusions Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options

Conditional deletion of <i>Atr</i> leads to a reduction in the size of mouse eyes.

<p>Extracted eyes (<b>A</b>) and dissected RPE whole mounts (<b>B</b>) from 30 day old mice following nuclear localized β-gal activity staining. Conditional deletion of <i>Atr</i> often resulted in the significant reduction of eye size (<b>A</b> and <b>B</b>). The conditional loss of ATR significantly reduced RPE petal length as defined by the distance from the optic nerve to the distal edge of the RPE (<b>C</b>). i <i>p^un/un</i> (n = 7); ii <i>Trp1-Cre^tg/o</i> (n = 6); iii <i>Atr^cond/+</i> (n = 6); iv (s) <i>Atr^cond/−</i> small (n = 10); iv (n) <i>Atr^cond/−</i> normal (n = 6) (<b>A</b>, <b>B</b> and <b>C</b>). Solid red lines indicate the distal edge of the RPE, small dashed red lines indicate the proximal edge of the RPE, large red dashed lines denote the region that is 0.6 of the petal length (petal length equals the distance between the optic nerve and the proximal edge of the RPE) and solid red circles indicate the optic nerve. Scale bar: 1 mM (<b>B</b>). For (<b>C</b>) α: comparison to <i>p^un/un</i> (<i>P<0.0001</i>); β: comparison to <i>Trp1-Cre^tg/o</i> (<i>P<0.0001</i>); γ: comparison to <i>Atr^cond/+</i> (<i>P<0.0001</i>) and δ: comparison to <i>Atr^cond/−</i> normal (<i>P<0.0001</i>); error bars indicate S.E.M.</p

Model for the relationship between ATR, homologous recombination and chromosomal stability.

<p>In the presence of replication stress from endogenous lesions, ATR is activated initiating an S-phase arrest to block aberrant merger of another replication fork at the same lesion. Some stalled replication forks will be substrates for HR (depicted here is the formation of a chicken foot structure that acts as a RAD51 substrate). If HR proceeds as normal, then the replication fork will be restored. However, we propose that this progression is dependent upon sufficient time to complete the HR reaction and possibly a more direct licensing of a later step in HR by ATR kinase activity (CHK1, BRCA1 and BLM, for example – not shown). If HR does not restore the stalled fork, then it may collapse, potentially leading to chromosomal breaks and the production of micronuclei. Thick lines are parental strand DNA, thin lines are daughter strand DNA, half arrowheads represent 3′ ends, the solid black triangle represents a DNA lesion and solid black circles represent RAD51 protein.</p

Spontaneous homologous recombination repair is decreased in the absence of ATR <i>in vivo</i>.

<p>Schematic representation of the <i>p^un</i> mutation (tandem duplication of exons 6–18) in which a HR-mediated event facilitates the deletion of one copy of the repeat resulting in the reversion of the mutant allele to a functional <i>p</i> gene. Reversion (HR) events are scored phenotypically by counting the numbers of single or groups of cells in the RPE with brown pigmentation in their cytoplasm. (<b>A</b>). Loss of one copy of <i>Atr</i> does not affect HR frequency (<i>i.e.</i> the number of <i>p^un</i> reversion events per RPE) (<b>Bi</b> and <b>ii</b>), whereas complete loss of ATR resulted in the significant decrease of HR frequency (<b>Bi</b> and <b>ii</b>). Within the outer clear region of <i>Atr^cond/−</i> normal eyes (<i>i.e.</i> no β-gal activity suggesting the presence of a single copy of <i>Atr</i>), the HR frequency was not different from <i>Trp1-Cre^tg/o</i> and <i>Atr^cond/+</i> in similar regions (<b>Biii</b>). Representative eye spot images from different genotypes in B. Pigmentation can be observed in the cytoplasm following a <i>p^un</i> reversion event in the RPE. Blue nuclei indicate nuclear-localized Cre activity (<b>C</b>). For (<b>B</b> and <b>C</b>) (s) <i>Atr^cond/−</i> small and (n) <i>Atr^cond/−</i> normal eyes; *<i>P<0.05</i> and ***<i>P<0.001</i>.</p

ATR promotes homologous recombination <i>in vitro</i>.

<p><i>I-SceI</i>-induced HR-mediated repair was significantly decreased in the DR-GFP U2OS cell line with reduced ATR expression (<b>A</b>). For (<b>A</b>) ***<i>P<0.001</i> and n = 3. (B) A representative image of ATR expression knockdown using siRNA in the DR-GFP U2OS cells (siCtr is scramble).</p

Morphological abnormalities of the RPE monolayer and increased chromosomal damage following the conditional deletion of <i>Atr</i>.

<p>Loss of either a single copy of <i>Atr</i> or both copies of <i>Atr</i> lead to morphological abnormalities of the RPE (A), a significant decrease in cell density (B) and a significant increase in micronuclei formation (C). i <i>p^un/un</i> (n = 5); ii <i>Trp1-Cre^tg/o</i> (n = 7); iii <i>Atr^cond/+</i> (n = 9); and iv (s) <i>Atr^cond/−</i> small (n = 23); (A, B and C). RPE whole mounts were stained for nuclear localized β-gal activity (black spots) and phalloidin (yellow) to identify nuclear material and cell boundaries, respectively. Red boxes indicate the 200 μm² region used for cell counting, solid red circles and white arrowheads mark individual cells and micronuclei, respectively (A). Error bars indicate S.E.M. (B and C). Scale bar: 25 μm (A). For (B) α: comparison to <i>p^un/un</i> (<i>P<0.001</i>); β: comparison to <i>Trp1-Cre^tg/o</i> (<i>P<0.001</i>) and γ: comparison to <i>Atr^cond/+</i> (<i>P<0.01</i>).</p