Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

doi:10.21203/rs.3.rs-1641376/v1

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Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

https://doi.org/10.21203/rs.3.rs-1641376/v1

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published 18 Apr, 2024

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Tailoring the best treatments for individual cancer patients is an important open challenge. Here, we build a precision oncology computational pipeline for PERsonalized single-Cell Expression-based Planning for Treatments In ONcology (PERCEPTION). Our approach capitalizes on recently published matched bulk and single-cell (SC) transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients' SC tumor transcriptomics. We start by showing that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on their SC-expression profiles. Our key result is that PERCEPTION successfully stratifies responders to combination therapy based on the patients’ tumor’s SC-expression, as tested in two recently published clinical trials, including multiple myeloma and breast cancer. Thirdly, studying the emergence of resistance via a recent SC non-small cell lung cancer (NSCLC) patients’ cohort, we show that PERCEPTION successfully captures and quantifies the development of patients’ resistance during treatment with tyrosine kinase inhibitors. Notably, PERCEPTION predictions markedly outperform that of bulk expression-based predictors in all these cohorts. In sum, this study provides a first-of-its-kind conceptual and computational method demonstrating the feasibility of predicting patients' response from SC gene expression of their tumors.

Precision oncology has made important strides in advancing cancer patient treatment in recent years, as reviewed in (Tsimberidou et al. 2020a; Huang et al. 2021; Bhinder et al. 2021; Singla and Singla 2020; Senft et al. 2017; Tsimberidou et al. 2020b). Much of the focus in the field has been on efforts to use FDA-approved sequencing assays to identify “actionable” mutations in cancer driver genes, to match patients to treatments (Tsimberidou et al. 2020a). These efforts have been further boosted by the progress made in DNA-based liquid biopsies, which further can help guide and monitor treatment (Siravegna et al. 2017; Heitzer et al. 2019; Sawabata 2020). However, a large fraction of cancer patients still do not benefit from such targeted therapies, and efforts are hence much needed to find ways to analyze other molecular omics data types to benefit more patients. Addressing this challenge, recent studies have begun to explore the benefit of collecting and analyzing bulk tumor transcriptomics data to guide cancer patient treatment (Beaubier et al., 2019; Hayashi et al., 2020; Rodon et al., 2019; Tanioka et al., 2018; Vaske et al., 2019; Wong et al., 2020, Lee et al., 2021, Dinstag et al., 2022). These studies have demonstrated the potential of such approaches to complement DNA sequencing approaches in increasing the benefit of omics-guided precision treatments to patients.

One key limitation of current genomic and transcriptomic treatment approaches is that they are mostly based on bulk tumor data. Tumors are typically heterogeneous and composed of numerous clones, making treatments targeting multiple clones more likely to diminish the likelihood of resistance emerging due to clonal selection, and hence potentially enhancing the overall patient’s response (Castro et al. 2021). Intra-tumor heterogeneity has been driving two major developments in recent years, the search for effective treatment combinations and the advent of single-cell profiling of the tumor and its microenvironment.

Large-scale combinatorial pharmacological screens have been performed in patient-derived primary cells, xenografts, and organoids and have already given rise to numerous combination treatment candidates (e.g., Wensink, et al. 2021, Yao et al. 2020, de Witte et al. 2020). Concomitantly, the characterization of the tumor microenvironment via single-cell omics has already led to important insights regarding the complex network of tumor-microenvironment interactions involving both stromal and immune cell types (e.g., Castro et al. 2021). It also offers a promising way to learn and predict drug response at a single-cell resolution. The latter, if successful, could guide the design of drug treatments that target multiple tumor clones disjointly (Shalek and Benson 2017, Adam et al 2020, Zhu et al 2017) and help us understand the ensuing resistance to better overcome it. However, building such predictors of drug response at a single cell (SC) resolution is currently challenging due to the paucity of large-scale preclinical or clinical training datasets. Previous efforts, including a recent computational method termed Beyondcell that identifies tumor cell subpopulations with distinct drug responses from single-cell RNA-seq data for proposing cancer-specific treatments, have focused on preclinical models but lack validation in patients at the clinical level (Kim et al 2016, Suphavilai et al. 2020, Fustero-Torre et al. 2021, Ianevski et al. 2021). Additional efforts to identify biomarkers of response and resistance at the patient level using single-cell expression are emerging for both targeted- and immuno- therapies, with remarkable results (Cohen et al 2021, Ledergor et al 2018, Sade-Feldman et al 2018). However, to date, harnessing SC patients’ tumor transcriptomics for tailoring patients’ treatment in a direct, systematic manner has remained an important open challenge.

Aiming to address this challenge, here we present a precision oncology framework for PERsonalized single-Cell Expression-based Planning for Treatments In ONcology (PERCEPTION). This approach builds upon the recent availability of large-scale pharmacological screens and SC expression data in cancer cell lines to build machine learning-based predictors of drug response based on the gene expression of single cells. We first show that the predicted viability for drugs with known mechanisms of action strongly correlates with the pathway activity it is targeting, visualizing our ability to predict at a single-cell resolution. Second, we show that PERCEPTION can predict the response to single and combination treatments in three independent screens performed in cancer and patient-tumor-derived primary cells, based on their SC-expression profiles. Thirdly, we show that PERCEPTION stratifies responders vs. non-responders to combination therapies in recently published multiple myeloma and breast cancer clinical trials from the patients’ tumor SC-expression profile. Fourthly, we show that PERCEPTION captures the development of resistance and cross-resistance to four different kinase inhibitors in a cohort of lung cancer patients with tumor SC-expression profiles during treatment. Notably, PERCEPTION markedly outperforms state-of-the-art bulk-based predictors in all three SC clinical cohorts considered. In sum, we provide a first-of-its-kind computational approach that demonstrates the exciting potential of SC gene expression-based precision oncology.

Overview of PERCEPTION

To predict patient response to therapy from the tumor’s SC-expression profile, we built a machine learning pipeline called PERCEPTION (Figure 1A, a detailed description is provided in Methods). PERCEPTION builds drug response models from large-scale pharmacological screens performed in cancer cell lines, for which both bulk and SC-expression are available. As matching cell-lines SC-expression data is still of a moderate scale, we designed a stepwise prediction pipeline: first, the prediction models are trained on large-scale bulk-expression profiles of cancer cell lines and then, in a second step, the models’ performance is further optimized by training on SC-expression profiles of cancer cell lines. To this end, we mined bulk expression (Ghandi et al 2020) and drug response profiles (PRISM) of 488 cancer cell lines (Table S1) from the DepMap database (Tsherniak et al 2017). The SC-expression profiles of these cell lines (N=205, Table S1) have been obtained from (Kinker et al. 2020). Drug efficacy is measured via the area under the curve (AUC) of the viability-dosage curve, where lower AUC values indicate increased sensitivity to treatment (Table S1). Due to a lack of large-scale screens in normal and immune cells for training, we could only train our drug response models on cancer cells in this study.

To build a predictor of response for a given drug, PERCEPTION performs the following two steps: 1. It first builds a regularized linear model of drug response using the bulk expression and drug response data available for ~300 cancer cell lines. 2. In the second step, we determine the number of genes used as predictive features (hyperparameter tuning) that maximize its ability to predict the response from SC-expression data, analyzing the ~170 cancer cell lines where additionally scRNA-seq profiles are available. The goal of this step is to build SC-expression prediction models of drug response. To evaluate the performance of an SC model in a given cell line, PERCEPTION predicts the response to a given drug for each of its individual cells, and the mean response over all those individual cells is taken as the predicted SC-based response of that cell line to that specific drug. The output of this machine learning pipeline is hence a drug-specific SC response model and a quantification of its predictive accuracy from SC-expression. Importantly, the latter is evaluated in an unseen test subset of the cell lines, employing a standard leave-one-out (one cell line) cross-validation procedure (Methods).

Building PERCEPTION models for FDA-approved cancer drugs based on the PRISM screen

We applied PERCEPTION aiming to build response models for 133 U.S FDA-approved oncology drugs tested in the PRISM drug screen (Table S2). The predictive performances for each of these drugs are provided in Figure 1B. We denoted models as predictive at a single-cell resolution if the Pearson correlation between their predicted (mean SC-response per cell line) vs. the observed viability on the leave-one-out test data was greater than 0.3. This threshold was chosen as it corresponds to the mean cross-screen replicate correlation observed among three major pharmacological screens (average cross-platform correlation across GDSC, CTD & PRISM ~ 0.30) at an individual drug level as previously reported (Corsello et al. 2020, refer to their Extended Figure 5C-D). We were able to build such predictive models for 44 out of 133 drugs (33% of the drugs, Table S2, Figure 1B). Studying the predictive accuracy of these 44 predictive models in a cross-validation manner for different kinds of transcriptomics inputs, including SC, bulk, and pseudo-bulk-expression (generated by summing up the gene-mapped reads across single cells, Methods), we reassuringly find that the predictive performance of PERCEPTION for SC-expression as inputs on these cell lines is comparable to that performance obtained using bulk-expression or pseudo-bulk as inputs (Figure 1C).

Visualization of PERCEPTION’s ability to predict viability at single-cell resolution in two case studies

We start by illustrating a few cases, where PERCEPTION’s ability to predict cell killing is visualized at single-cell resolution. We demonstrate this for two drugs where the pathways involved in the mechanism of action of the drugs are well characterized, nutlin-3 and erlotinib. The first case focuses on the canonical antagonist, nutlin-3, whose mechanism of killing involves the inhibition of the interaction between MDM2 and the tumor suppressor p53; thus, MDM2 high activity is a known response biomarker to nutlin-3 treatment (Arya et al. 2010). Via PERCEPTION, we built a response model for nutlin-3, where the correlation between the predicted and observed response on the test set was R = 0.598, P=1.2E-16. Using this model, we predicted the post-nutlin-3 treatment killing for each of 3566 single cells across nine p53 wild-type lung cancer cell lines. Across these single-cells, PERCEPTION’s predicted killing and MDM2 expression are strongly correlated across the individual cells screened (Pearson R= 0.50, P<2E-16, as visualized in Figure 1D), as expected. Of interest, one can discern a few sub-clones that have predicted pre-existing nutlin-3 resistance (Figure 1D-arrow highlight).

In the second case, we performed a similar analysis to study and visualize PERCEPTION’s ability to predict the response to erlotinib, which targets oncogenic, activating mutations of epidermal growth factor receptor (EGFR) and has been used to treat NSCLC. The correlation between the predicted and observed response on the test set for this prediction model was Pearson’s R= 0.50, P<1E-05. As before, we find that the predicted killing of erlotinib and EGFR pathway activity (estimated via the mean expression of a published EGFR signature (Cheng et al. 2020)) are significantly correlated across individual cells (Pearson R= 0.34, P<2E-16, Figure 1E). Similar visualizations and findings with other EGFR inhibitors developed more recently (afatinib, icotinib, lapatinib, osimertinib) with even stronger correlations strength and other FDA-approved drugs with well-characterized mechanisms of action are provided in Extended Figure 1.

Evaluating PERCEPTION models built using the PRISM screen in the GDSC and an independent lung cancer drug screen

We next evaluated the performance of PERCEPTION models that are built using the PRISM screen on two other large-scale cell-line screens, for which we have matching SC cell-line data (Garnett et al. 2012, Nair et al. 2021). To this end, we first identified the drugs that are shared between the PRISM and GDSC screens (N=191, Table S3, quality control and model building steps in Methods). We were able to build PRISM-based PERCEPTION predictive models for 16 drugs that have been screened in both and have a substantial positive correlation between their AUC values (Pearson R > 0.3, p-value < 0.05 in all cell lines). In building these models, we held out 80 randomly selected test cell lines that were used to test the performance of the resulting PERCEPTION models in each of the two screens. As a starting point for this comparison, we note that the mean correlation between the experimental viabilities reported in GDSC vs. PRISM (screen concordance) across the 80 shared test cell lines was only 0.44 (Figure 2A, green, Methods), and this is one justification for the correlation threshold of 0.3 used in our modeling. For the same testing set, the mean correlations between the predicted vs. observed viabilities, using the SC-based PERCEPTION models are 0.38 in the PRISM dataset (on which they were built, Figure 2A, blue). It is considerably lower but is still considerable in the GDSC screen, attaining a correlation of 0.28 there (Figure 2A, orange). As one may expect, the prediction performance of the PRISM-based models in the GDSC test set is correlated with the concordance between the experimentally measured drug’s viability profiles in the two screens (Pearson R=0.49, P=5.89E-02; Figure 2B, Table S4). Of note, as the range of PERCEPTION predicted values is typically smaller than those observed in the screens (Extended Figure 2), we use scaled, normalized predicted AUC scores (z-score) in the further analyses reported below.

Finally, we tested and demonstrated the predictive power of PERCEPTION in another independent, yet unpublished drug screen in NSCLC cell lines (Nair et al. 2021), which includes monotherapies and combinations of 14 cancer drugs across 21 shared NSCLC cell lines. As above, the PERCEPTION predictions are based on the SC data of the pertaining cell lines (shown in Figure 2C-F). As the main emphasis of our study is on building response predictors in patients, these validations and their results are described in more detail in the Supplementary material (Extended Figure 3A-F, Table S5, Methods, Supplementary Note 1). Similarly, PERCEPTION also successfully stratified resistant vs sensitive patient-derived primary cells (PDC) from head and neck cancer using their SC-expression (mean AUC=0.75, Extended Figure 4-6, Supplementary Note 8, Table S6). In sum, these analyses demonstrate PERCEPTION’s ability to predict drug monotherapy and combination response in independent cancer cell lines screens and PDCs based on their SC-expression.

PERCEPTION predicts patients’ treatment response in the DARA-KRD combination multiple myeloma trial

We next turned to test the ability of PERCEPTION models to predict patient response based on pre-treatment SC transcriptomics from their tumors, which is our main goal. Very few such datasets exist with considerable coverage of sequenced cancer cells in general, and specifically involving drugs currently having PERCEPTION models. We begin with the largest such dataset published to date, including data for 41 multiple myeloma patients. The patients were treated with a DARA–KRD combination of four drugs - daratumumab (monoclonal antibody targeting CD38), carfilzomib (proteasome inhibitor), lenalidomide (immunomodulator), and dexamethasone (anti-inflammatory corticosteroid) (Cohen et al. 2021). The SC-expression and clonal (transcriptional cluster) composition and treatment response labels (as originally determined by (Cohen et al. 2021)) were available for 28 tumor samples of these patients (Figure 3B). Patient response was measured via tumor size estimates in radiological images.

We succeeded in building predictive PERCEPTION response models for two of the four drugs in the trial (carfilzomib and lenalidomide, Supplementary Note 2). Using these models, we predicted the combination response for a given patient via the following two steps (Figure 3A): (1) Predicting the combination response of each individual clone (transcriptional cluster) in a given tumor: We first predicted the combination response for each clone (one of three transcriptional clusters originally identified in (Cohen et al. 2021)) based on its mean expression profile across all the cells composing it. To this end, we first predict the response for each of the two drugs in the combination separately based on their respective PERCEPTION models. Second, we then take the maximal killing among these two drugs as the predicted killing of the combination for that specific clone, following the Independent Drug Action principle (IDA, Ling et al. 2020).

(2) Second, having predicted the combination effect on each of the clones present in a given tumor, we predict the overall patient’s response as the predicted response of the least responsive clone, assuming that it is likely to be selected by the treatment and dominate the overall tumor’s response (Figure 3A). This notion is further motivated by observing that the predicted response of the most resistant clone indeed best stratifies the responders vs. non-responder patients among four different alternative strategies that we devised and tested on this dataset using SC-expression (Supplementary Note 2, Methods). This strategy is then fixed and used in all other patients’ analyses shown herein.

Employing this approach, we have applied PERCEPTION to predict the treatment response of each of the 28 patients in the trial. Figure 3C shows the predicted viability of the combination at a clonal level for each patient. As evident, the resulting predicted response scores (1- predicted viability) are significantly higher in responders vs. non-responders (Figure 3D) and, importantly, can successfully predict treatment response with quite a high accuracy (ROC-AUC of 0.827, Figure 3E).

We compared PERCEPTION stratification performance to four different kinds of control modes (Supplementary Notes 2 and 3). First, we repeated the analysis using pseudo-bulk expression (applying the predictor to the mean expression over all the cells in the tumor), which yields a poor ROC-AUC of 0.56. Second, we predicted patients’ responses by taking the mean viability across all single cells in a tumor sample (the strategy we used for predicting response in cell lines and PDCs), yielding a predictive signal with ROC-AUC of 0.64 (Supplementary Note 2). Third, we built and tested three different types of random models, built by (1) Shuffling the viability labels in the cell lines, by (2) randomly selected gene signatures, and finally (3) using non-predictive models of other drugs (Methods). As expected, these models yielded significantly lower stratification power than that obtained by PERCEPTION (empirical P-values over 1000 instances of P=0.002, P<0.001, and P<0.001, respectively). Finally, we compared the PERCEPTION stratification performance with that of published state-of-the-art bulk-expression machine learning response prediction models (Tsherniak et al. 2017) and of bulk-based-only PERCEPTION models (that are not tuned on SC-expression). Using these two models yields quite inferior performance, with AUCs of 0.62 ± 0.001 and 0.52 ± 0.001, respectively (Extended Figure 7, Supplementary Note 2). Overall, these results testify to the markedly superior performance obtained by PERCEPTION compared to a large variety of alternative expression-based predictors.

PERCEPTION predicts patients’ response to CDK inhibition in the FELINE breast cancer trial

Using the prediction approach described in the previous section, we next tested PERCEPTION’s ability to predict patient response in the FELINE breast cancer clinical trial (Griffiths et al. 2021). This clinical trial includes three treatment arms: endocrine therapy with letrozole (Arm A), an intermittent high-dose combination of letrozole and CDK inhibitor ribociclib (Arm B), and a continuous lower dose combination of the latter (Arm C). SC-expression and treatment response labels were available for 33 patients (Arm A=11 samples, Arm B= 11 and Arm C=11; Table S7). Patient response was determined via tumor growth measurements from mammogram, MRI, and ultrasound of the breast.

We could only build a (albeit borderline) predictive PERCEPTION response model for CDK inhibitor ribociclib (we note that with a Pearson R=0.26, P=1.5E-03), and thus we focused our analysis on the combination arms B and C that include it (Figure 4A). We processed the SC-expression profiles of the tumor cells as described in (Griffiths et al. 2021) and identified 38 transcriptional clusters/clones that are shared across the patients (Extended Figure 8A-C, Supplementary Note 3, Methods). Patient response was predicted based on the pretreatment samples, following the exact same strategy employed in the multiple myeloma case. As the number of patients in each arm B,C is quite small we predicted the response of the patient pre-treatment samples in aggregate. The resulting predicted viability of the non-responders is higher than that of the responders (Wilcoxon rank-sum test, one-sided P=0.05, Figure 4B), as expected. PERCEPTION successfully stratified the responders vs. non-responders with an ROC-AUC of 0.776 (Figure 4C). As in the multiple myeloma case, PERCEPTION’s stratification performance is higher than three random control models, including (1) 1000 PERCEPTION models generated by shuffling the viability labels (P-value=0.042), (2) 1000 randomly generated gene signatures (P=0.036), and (3) 200 non-predictive PERCEPTION models (P=0.01). It also outperforms two bulk expression-based prediction models, including (1) published drug response models (Tsherniak et al. 2017) (AUC=0.60 ± 0.009) and (2) PERCEPTION bulk expression-based models that have not been tuned on SC-expression (AUC=0.64 ± 0.012) (Methods, Extended Figure 8D). Notably, computing the response using the strategy we employed for cell line predictions (based on computing the mean viability across all single cells) yields an AUC of 0.735. While being lower than that achieved by our chief, most-resistant clone based strategy, it still markedly outperformed the numerous control models (Extended Figure 8E, Supplementary Note 3).

PERCEPTION captures the development of resistance to multiple tyrosine kinase inhibitors in lung cancer patients

We next tested if PERCEPTION can capture the development of clinical resistance during targeted therapy treatment in patients. To this end, we analyzed a recently published cohort with a scRNA-seq profile of 24 NSCLC patients with 14 pre- and 25 post-treated biopsies (Maynard et al. 2020) (Extended Figure 9A-F, Table S8). In total, patients in this cohort were treated with four different tyrosine kinases including erlotinib (a 1^st generation EGFR inhibitor), dabrafenib (a serine/threonine kinase inhibitor), osimertinib (3rd generation EGFR inhibitor), and trametinib (a MEK inhibitor). Based on the notion that the resistance to these targeted therapies frequently increases as the treatment prolongs, we hypothesized that the predicted response for a given post-treatment biopsy would decrease (reflecting an increase in resistance to that treatment) as time elapses from the treatment start.

To study this hypothesis, for each post-treatment biopsy, we defined its estimated “Extent of Resistance'' to a given treatment as the difference between its PERCEPTION predicted response vs. the baseline predicted response. The latter was computed as the mean predicted viability across all pre-treatment biopsies (as the majority of the samples were not matched, precluding an overall pairwise matched comparison). We find that the extent of resistance to treatment increases with the elapsed time since the start of treatment, but only in the patients reported to acquire resistance (progressive disease, Spearman Rho=0.634, P=0.026, Figure 5A, N=17). We also found that this positive correlation between the elapsed treatment time and the estimated extent of resistance holds true when patients receiving different drugs are analyzed separately (Extended Figure 10A), when controlling for prior treatments (Extended Figure 10B), when individual patients are analyzed separately (Extended Figure 10C) and when controlling for tumor stage (Extended Figure 10D). We also note that the extent of predicted resistance is significantly higher in post-treatment biopsies collected from the patients with Progressive Disease vs Residual disease (Wilcoxon Rank sum P<0.002, Stratification ROC-AUC=0.88, Figure 5B). Notably, we do not observe this strong positive correlation but rather a negative trend in patients that have responded well to the treatment (Residual Disease, N=7, Spearman Rho= -0.67, P=0.11, Figure 5A). The observed increase in the predicted extent of resistance to treatment with elapsed treatment time occurred specifically in patients that acquire resistance. We note that we do not observe this correlation when considering only the bulk-expression (Extended Figure 10B, Supplementary Note 4).

We next analyzed the subset of patients with matched biopsies, including five patients with two biopsies each and one patient with four biopsies. Analyzing these samples in a matched manner, we find that the correlation between treatment elapsed time and the estimated extent of resistance holds true in the matched cases, and only in the patients that have acquired resistance (regression interaction P=0.003). Of particular interest is a case of a single patient (TH179), treated with dabrafenib, that had four biopsies at two different time points and developed progressive disease. The predicted viability to dabrafenib of the four tumor biopsies taken after 331 and 463 days of start of treatment is significantly higher than pre-treatment biopsies (Figure 5C). Furthermore, the predicted viability of all three biopsies from day 463 is significantly higher than the biopsy from day 331. Notably, we find that the abundance of the top 50% predicted resistant clones increases while the abundance of the bottom 50% predicted resistant clones decreases with the elapsed time since the start of treatment, as one would expect (Figure 5D, Methods). The rate of increase of abundance is significantly higher in the top 50% of the predicted resistant clones vs the bottom 50% (Figure 5D, Methods). Taken together, these results testify that PERCEPTION can capture and quantify the emergence of treatment resistance as the disease progresses.

To prioritize candidate drugs available in this cohort whose treatment may overcome the resistance acquired, we asked if the development of resistance to a drug can induce either cross-sensitivity or cross-resistance to the other drugs (Plucino et al. 2012). We focused on the patients (Table S8) that acquired resistance and computed the PERCEPTION response predictions for each of these drugs and the correlations between these drug sensitivity predictions across these patients (Figure 5E, Methods). PERCEPTION predictions suggest that the development of resistance to erlotinib would induce a cross-sensitivity to gemcitabine (Figure 5F, Top-Left panel, Pearson’s R= -0.94, P=0.06) and cross-resistance to dabrafenib (Figure 5F, Top-Left panel, Pearson’s R=0.91, P=0.09). A literature survey (Methods) revealed that gemcitabine treatment can overcome erlotinib resistance in cancer cell lines through downregulation of Akt (Bartholomeusz et al. 2011). In patients, a combination of gemcitabine+erlotinib in pancreatic cancer in phase III trial has shown a higher overall and progression-free survival (Moore et al. 2016, Shin et al. 2016). In contrast, the addition of trametinib to erlotinib did not significantly improve survival in a phase I/II clinical trial (Luo et al. 2021). In sum, our analysis supports the possibility that erlotinib resistance may induce cross-sensitivity to gemcitabine, which may be of interest for future testing.

We present PERCEPTION, a first of its kind computational pipeline for systematically predicting patient response to cancer drugs at single-cell resolution. We demonstrate its application for predicting response to monotherapy and combination treatment at the level of cell lines, patient-derived primary cells, and in predicting patient response in three recent single-cell clinical cohorts, spanning multiple myeloma, breast cancer, and lung cancer. We find that incorporating the transcriptional clonal information of the tumor into the prediction process improves the overall accuracy. For a given patient, the transcriptional clone with the worst response, that is the most resistant pre-treatment clone, best explains their overall response to treatment.

We note that predicting cell-line response using the most-resistant-clone strategy (the one used for predicting the response in clinical trials) can also quite successfully stratify resistant vs. sensitive cell-lines, however, with lower performance (Extended Figs. 11, 12, Supplementary Note 5) than the mean-response strategy that we used for predicting cell-lines response (mean predicted response computed over all the individual single cells from a cell line). We think that a likely explanation for the difference in the performance of the mean vs the most-resistant-clone based prediction strategies observed in the cell-lines vs patient’s data could be that the clinical responses are measured at much longer time-scales in the patients (months) than in the cell lines (within days), thus providing time for the selection of the most resistant clone. This underscores the importance of considering the repertoire of a given tumor’s transcriptional clones in predicting its response to therapy. It is also interesting to note that PERCEPTION has succeeded in predicting response to combination therapies even when we had predictive models for only subsets of the drugs employed. This lays a basis for the hope that the results presented here could be considerably improved once we are able to build predictive models for many more FDA approved drugs.

The scope and utility of our analysis are currently considerably limited by the scarce availability of pre-treatment SC-expression patient datasets with treatment response labels. One can quite confidently estimate that the accuracy and breadth of SC-based drug response predictors will markedly increase in the foreseen future with the growing availability of such data. In essence, it is another incarnation of the chicken and egg scenario – these relatively costly datasets will only be generated on a large scale when their clinical utility becomes more apparent, and the current paucity of these datasets yet impedes further progress. Hence, the current demonstration of their potential value, coupled with the basic intuition that one needs to target multiple clones in tumors to achieve long-enduring responses, will hopefully serve to drive the generation of relevant datasets in clinical settings, moving forward. Considering that the average annual cost of treating a cancer patient in the US is currently around $150K, the current cost of about $15K for sequencing a tumor to optimize treatment is one order of magnitude smaller (Mariotto et al. 2011), and at least in our minds, a well-justified expense that is ought to be carefully studied and considered moving forward, both by assembling additional SC datasets in clinical trials and by developing computational prediction methods to capitalize on them.

Consequently, one can further expect that SC-based drug response predictive models would further improve when such datasets would become more available. But beyond that, they could be further improved by considering cancer type-specific cell lines, whenever a large number of such models become available for each cancer type. We note that the quality of our response models would also depend on the quality of the SC-expression profiles available e.g., their depth, drop-out rates, etc. We deliberately chose not to impute the SC data given the recent reports that dropouts are limited to non-UMI-based SC-expression methods and otherwise likely reflect true biological variation (Svensson et al. 2020, Cao et al. 2021). A key limitation of our pipeline is a lack of ability to predict drug effects on immune and normal cells in the tumor microenvironment, which is needed to estimate the toxicity and side effects of different combinations. A major push to future SC-based precision oncology development will come from large-scale drug screens of drugs in noncancerous cell lines, currently very scarcely available. Those will enable the construction of predictors of drug killing of non-tumor cells, using an analogous pipeline to the one presented here for tumor cells. Finally, our results demonstrate that tracking the drug response expression in post-treatment biopsies could help follow the evolution of drug resistance at a single-cell resolution and help guide the design of future personalized combination treatments that could significantly diminish the likelihood of resistance emergence.

Finally, one may envisage the application of PERCEPTION going beyond patient stratification, to the identification of new combination therapies for treating patients in a cancer type of interest. Such combinations would ideally optimally target the different individual clonal clusters, by killing as many of those with a minimal number of drugs as possible. Supplementary Notes 7, 8, and Extended Figs. 13, 14 present such an application, to give the interested reader an idea of this potential application. It is not included in the main text of this paper, as the latter’s focus is on stratifying patients to therapy, and additionally, currently our ability to test and validate the top emerging combination predictions is very limited.

In summary, this study is the first to demonstrate that the high resolution of information from scRNA-seq could indeed be harnessed to predict the treatment response of individual cancer patients in a systematic, data-driven manner. It is our hope that the results shown will herald many more such studies, rather sooner than later.

Data collection

We first collected the bulk-expression and drug response profiles generated in cancer cell lines curated in the DepMap (Tsherniak et al. 2017) consortium from Broad Institute (version 20Q1, https://depmap.org/portal/download/). The drug response is measured via area under the viability curve (AUC) across eight dosages and measures via a sequencing technique called PRISM (Corsello et al. 2020). In total, we mined 488 cancer cell lines with both bulk-transcriptomics and drug response profiles. We next mined SC-expression of 205 cancer cell lines (280 cells per cell line) generated in (Kinker et al. 2020) and distributed via the Broad Single-cell Portal. The metadata, identification, and clustering information were also mined from the same portal (https://singlecell.broadinstitute.org/single_cell/study/SCP542/pan-cancer-cell-line-heterogeneity#study-download).

For the multiple myeloma data set and the breast cancer data set all human subjects data are coded data from two published papers (Cohen et al. 2020; Griffiths et al. 2021). For the lung cancer data, we combined published data from (Maynard et al. 2020) with unpublished data provided by W.W., D.L.K., C.M.B., T.G.B. on December 9th, 2021. Those data were anonymized for the other authors and were obtained with informed consent from all study participants based on human subjects protocols (CC13-6512 and CC17618, C. M. B. Principal Investigator) approved by an IRB at UC San Francisco and based on clinical trial NCT03433469.

The PERCEPTION pipeline

A response model for a drug is built via the following two steps: 1. Learn from bulk and 2. optimize using SC expression. We first divided all the cancer cell lines into two sets - 1. Cell lines where bulk-expression is available, and SC-expression is not available (N=318) 2. Cell lines where SC-expression is available (N=170). The first set is used during learning from bulk (Step 1, expanded below) and the second in optimizing using SC expression (Step 2).

Step 1: Learn from bulk: As a feature selection step, we first identified genes whose bulk-expression is correlated with drug viability profile (using the Pearson correlation). We considered the Pearson correlation Pc(d, g) between drug d and gene g as a measure of information in a gene expression profile and ranked each gene based on the strength of the correlation. While considering the top X genes, where X is a hyperparameter optimized in the next step, we built a linear regression model regularized using elastic net to predict the response to d in five-fold cross-validation, as implemented in R’s glmnet (Friedman et al. 2010).

Step 2: Optimize using SC-expression: We built the above model using a Bayesian-like grid search of various possible values for X (range 10-500), where the model with the best performance using SC-expression input of 169 cell lines (left one out for testing) was chosen. We finally measured the model performance in a leave one out cross-validation using the left-out cell line, which was not used in either model building or hyperparameter optimization. Performance was measured using Pearson’s correlation between the predicted response and the actual response.

Cross-platform comparison of PERCEPTION performance

The pharmacological drug screens performed by the PRISM and GDSC studies are based on two independent platforms. The GDSC data were downloaded from the DepMap portal (Downloaded: April 15, 2020, https://depmap.org/portal/download/). To compare the performance of PERCEPTION across two independent screening platforms and test if the expression signature captured by our drug response models can be translated across the domains, we tested according to the following multi-step procedure:

1. Of the 347 cell lines in common with drug response in both GDSC and PRISM, there are 120 cell lines with SC-expression data in (Kinker et al. 2020). We selected at random 80 cancer cell lines with SC-expression data and pharmacological screens in GDSC and PRISM,

2. We considered all the drugs (N=191) that were screened in both PRISM and GDSC, from which we selected a subset of drugs (N=28) with a concordant response between PRISM and GDSC (Pearson rho > 0.3 and p-value < 0.05; at least 20 cell lines with responses per drug in both GDSC and PRISM) in the 267 cell lines in common between the two screens excluding the cell lines in the testing set.

3. For each of the drugs selected in step 2, we ran the PERCEPTION pipeline with one necessary change in the set of cell lines used. Specifically, in Step 2, the parameters were optimized on SC-expression of 90 cell lines (excluding the 80 test cell lines) instead of the default 170 cell lines which have response data in PRISM.

4. Finally, we applied the resulting response models to the testing dataset and compared the predicted AUC values to the experimental responses from GDSC and PRISM. We used the Pearson correlation coefficient as the measure to compare the performance between the screens and predicted responses.

PERCEPTION prediction of monotherapy and combination response in a lung cancer cell lines screen

We first performed a qualitative test of the drug screen mined from (Nair et al. 2021), where the response is measured via the AUC of the dosage-viability curve across eight dosages. To this end, we compared this screen to a previous high-quality screen called PRISM (Corsello et al. 2020). Specifically, we leveraged the fact that the two screens have common drugs and share some cell lines. Focusing on this set of cell lines and drugs, for each drug, we computed a correlation between the viability profile in the screen from Nair et al. and PRISM (Extended Figure 3A). We reasoned that the drugs with correlated profiles in the two screens (Pearson Rho>0.3, defined as concordance score) are consistent across the two screens, suggesting that they are high quality. Independently, we also note that the concordance score of drugs’ response profile across screens is correlated with our predictive performance (Pearson Rho>0.39; P<0.019, Extended Figure 2A), suggesting that our model is capturing the robust signal across screens of these drugs. In this screen, we defined AUC data points <1 as high-confidence and we filtered out the other data points as typically an AUC larger than 1 is due to noise in the data as we see higher variability in doses that do not inhibit.

We focused on data points for 14 FDA-approved drugs in 21 cell lines that passed the above filter for which we could build predictive PERCEPTION models (Pearson’s R>0.3, P<0.05). We assessed their predictive performance vs. drug screen data measured for monotherapy and two-drug combinations of these drugs across 21 NSCLC cell lines in five dosages (Table S5). Using SC expression for these NSCLC cell lines profiles in (Kinker et al. 2020) (300 cells per cell line), we used the PERCEPTION models to predict the response to each drug in each cell line by computing the mean predicted viability across all the single cells of that cell line. We next tested PERCEPTION’s ability to predict the response to combinations of these 14 drugs studied in this screen (Table S5). A combination response in a given cell line was predicted by adopting the independent drug action (IDA) model across all the single cells from that cell line (Ling et al. 2020); i.e., the predicted combination response of N drugs is the effect of the single most effective drug in the combination. Performance was measured using ROC-AUC. Throughout our work, combination response is predicted using the IDA principle.

PERCEPTION’s prediction in patient-derived head and neck cancer cell lines

The single-cell expression of the five head and neck squamous cell cancer (HNSC) patient-derived cell lines and their treatment response for eight drugs and combination therapy at two different dosages were obtained from Suphavilai et al. 2020. For these drugs, PERCEPTION was unable to build drug response models with Spearman correlation between their predicted vs. experimental viability greater than 0.3 using PRISM screens. Therefore, we incorporated two main changes to the PERCEPTION pipeline:

1. Drug response from GDSC screens (response from > ~800 cell lines for these drugs) were used to build models,

2. Only the top 3000 highly expressed genes (with fewer dropouts in the HNSC dataset) in common between the bulk expression and PDC datasets were considered in the pipeline. For the drugs for which PERCEPTION was able to build models, we applied the models on the PDC cell lines and obtained the predictions for each individual cell. The patient-level monotherapy response for a given drug is represented by the mean response of all the cells included in a patient’s PDC sample. In the case of drug combinations, for a given cell, its combination response is computed using IDA, i.e., the predicted combination response of N drugs is the effect of the single most effective drug in the combination (Ling et al. 2020, IDA). The patient-level combination response was represented by the mean of the combined response of all the cells in a patient's PDC sample.

Predicting combinations response in multiple myeloma patients

Response labels, SC-expression of patients’ tumors (MARS-seq), clustering annotation and mean cluster expression were mined from the original publication (Cohen et al. 2021). We only used and focused on the cells annotated as malignant. The steps to predict the combination response of a patient can be divided into a two-step process: Step 1. Predict the combination response of each clone in that tumor, Step 2. Predict the patient’s response from the clone-level combination response. To this end, we first tried to build PERCEPTION response models for the four treatments used in the combination therapy. However, we were able to build predictive response models for only carfilzomib and lenalidomide. We first predicted the combination response for each transcriptional cluster (or simply referred to here as a “clone”). To this end, we predicted the response for each of the two drugs separately and computed the killing using the Independent Drug Action (IDA) principle i.e., the predicted combination response of N drugs is simply the effect of the single most effective drug in the combination (Ling et al. 2020). To overcome the challenge of the discrepancy of dosage used in the clinic vs. pre-clinical testing where our models are built, we z-scale our predicted response profile of a drug across clones, where this z-score predicted response represents the relative response of a clone compared to all the other available in the cohort.

In Step 2, we use this clone-level combination killing profile in a patient to predict the overall patient’s response. We considered the predicted response of the least responsive clone found in each patient as that overall patient’s response. This is based on the notion that it would be selected by the treatment and dominate the overall tumor. Performance was measured using ROC-AUC. For our model building control, we built random models using either shuffled labels, randomized features in the regression model, or a non-predictive model of another drug in the screen for 1000 times and computed the number of times that the stratification power denoted by AUC is higher than our original model. This proportion is provided as an empirical P-value.

Predicting combinations response in breast cancer clinical trial analysis

The pre-filtered 10X based single-cell RNAseq count data and the cell type annotations of the 65 breast cancer samples (34 patients) were downloaded from GEO (GSE158724). We considered only the cells annotated as tumor cells in our analysis. As defined in the primary publication of the dataset (Griffiths et al. 2021), we applied Seurat (v.4.0.5). We filtered out samples with fewer than 100 cells. We used the reciprocal principal-component analysis integration workflow to integrate the tumor cells from the remaining samples (Hao et al. 2021). The data were normalized using the SCTransform function and the top 5000 variably expressed genes and the first 50 PCs were used in the anchor-based integration step. The first fifty PCs and a k.param value of 20 were used to identify neighbors and the resolution was set to 0.8 to find distinct clusters. We identified 36 different clones, of which only 16 clones were found in the pretreated samples from patients in Arms B and C. The SC expression of 16 clones was considered in the drug response prediction analysis. The patient response information was obtained from Table S12 in (Griffiths et al. 2021).

The default PERCEPTION pipeline was used to build drug response models except for a single change. The top ~2500 highly expressed genes (ranked by the total number of non-zeroes across all the cancer cells) in the breast cancer dataset that are in common with the cancer cell line bulk expression data were used in the pipeline. The resulting models were used to predict response at the patient level in a similar manner to what we did for the multiple myeloma data. The controls for model building were also tested for the breast cancer data similarly to the testing we did for the multiple myeloma data.

Building bulk-based drug response models to distinguish responders from non-responders

We built bulk-based drug response models to compare their performance vs PERCEPTION models in stratifying responders from non-responders in the two clinical trials. To build drug response models based on bulk expression data, we considered all ~500 cell lines with bulk expression and PRISM-based drug response. For each drug, we randomly divided the data in training (1/3rd of the cell lines) and test set (2/3rd of the cell lines). As a feature selection step, we first identified genes whose bulk-expression is correlated with the drug viability profile (Pearson R) in the training set. We considered Pc(d, g) as a measure of information in a gene expression profile and ranked each gene based on the strength of the correlation. While considering the top 100 genes, we built a linear regression model regularized using an elastic net to predict the response to in leave one out cross-validation, as implemented in R’s glmnet (Friedman et al. 2010). The resulting model performance was validated on the testing dataset.

To build state-of-the-art drug response models as defined in (Tsherniak et al. 2017), we generated random-forest-based models in a similar framework as defined above. To make sure that the gene features used in the resulting model predictors are actually detected to be expressed in the patient SC-dataset, we consider genes that overlap in both the cell line bulk expression data and patient SC-dataset to build the models. For each drug, we repeated the above model-building steps 100 times and presented the mean and standard error of their performances in stratifying responders from non-responders in their respective clinical trials.

Predicting the development of resistance to multiple tyrosine kinase inhibitors trial in lung cancer patients

The SC-expression profiles of 39 biopsies from 25 patients were provided by the authors of (Maynard et al. 2020). The clinical annotations were mined from the original publication, specifically Supplementary Table 2. Similar to previous sections, we focused only on the subset of single cells labeled in the publication as malignant. Seurat clustering was performed with the resolution = 0.8, dims = 10, number of features = 2000, scale.factor = 10000, log normalization method with minimum cells in a cluster required to be > 3 and minimum features required to be > 200, to identify a total of 16 clones. The expression of each transcriptional cluster/clone in a patient is the averaged expression across all the single cells associated with that cluster in that given patient. We successfully built drug response models for dabrafenib, erlotinib, gemcitabine, osimertinib and trametinib. The response observed in the most resistant clone of a patient is considered as the PERCEPTION’s predicted response. We primarily studied the development of drug resistance in the trial. To this end, we defined a term called “Extent of Resistance” of a drug, which is a difference between a drug's predicted viability from PERCEPTION and the predicted baseline viability. The predicted baseline viability is defined as the average predicted viability of the respective treatments in all treatment-naive samples. This difference in response from the naive state denotes the extent of resistance and is thus named accordingly. We computed correlations using both Spearman and Pearson to test and identify robust correlations.

Literature survey of cross-resistance and cross-sensitivity

To search for evidence available in published papers for a cross-resistant or cross-sensitive drug pair, we used the search term “drug X AND drug Y” e.g., erlotinib AND gemcitabine, in the PubMed search portal https://pubmed.ncbi.nlm.nih.gov/ on December 26, 2021. The resulting clinical trials in the first fifty matches, sorted by best match, were manually surveyed for outcomes. For pre-clinical evidence for or against, non-clinical studies testing the combinations were manually surveyed.

Tracking the change of abundance vs predicted resistance of a clone

We first computed and ranked all clones with at least two data points at different time points by their mean predicted resistance across all samples they are present in. For each clone, we next computed the rate of change of abundance i.e. slope of the best fit line of abundance vs biopsy time from the start of treatment. Finally, we compared this “rate of change of abundance” vs “mean predicted resistance” of each clone.

Data availability

The entire collection of the processed datasets used in this manuscript, including pre-clinical models of cancer cell lines and PDCs, can be accessed via the following Zenodo repository ().

Code availability

The study’s scripts to replicate each step of results and plots can be accessed via the following Github repository (https://github.com/ruppinlab/SCPO_submission). We used open-source R version 4.0 to generate the figures. Wherever required, commercially available Adobe Illustrator 23.0.3 (2019) was used to create the figure grids.

Acknowledgments

This research was supported in part by the Intramural Research Program of the National Institutes of Health, NCI, NIH grants R01CA231300 (T.G.B.), R01CA204302 (T.G.B.), R01CA211052 (T.G.B.), R01CA169338 (T.G.B.), and U54CA224081 (T.G.B.). This work used the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). We acknowledge and thank the National Cancer Institute for providing financial and infrastructural support. Thanks to Kun Wang, Sheila Rajagopal and Ze’ev Ronai for their valuable feedback and discussion. Special thanks to Jason I. Griffiths and Andrea H. Bild for clarifying the patient response data in (Griffiths et al. 2021) and for their helpful feedback.

Competing interests

S.S., R.V., A.A.S. and E.R. are inventors on a provisional patent application covering the methods in PERCEPTION. E.R. is a co-founder of Medaware, Metabomed, and Pangea Biomed (divested from the latter). E.R. serves as a non-paid scientific consultant to Pangea Biomed, a company developing a precision oncology SL-based multi-omics approach, with emphasis on bulk tumor transcriptomics. T.G.B. is an advisor to Array/Pfizer, Revolution Medicines, Springworks, Jazz Pharmaceuticals, Relay Therapeutics, Rain Therapeutics, Engine Biosciences, and receives research funding from Novartis, Strategia, Kinnate, and Revolution Medicines. The work in the laboratory of C.H.B. was funded in part by Amgen and Novartis. The rest of the authors declare no conflict of interest.

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Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

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Abstract

Figures

Introduction

Results

Overview of PERCEPTION

Building PERCEPTION models for FDA-approved cancer drugs based on the PRISM screen

Visualization of PERCEPTION’s ability to predict viability at single-cell resolution in two case studies

Evaluating PERCEPTION models built using the PRISM screen in the GDSC and an independent lung cancer drug screen

PERCEPTION predicts patients’ treatment response in the DARA-KRD combination multiple myeloma trial

PERCEPTION predicts patients’ response to CDK inhibition in the FELINE breast cancer trial

PERCEPTION captures the development of resistance to multiple tyrosine kinase inhibitors in lung cancer patients

Discussion

Methods

Data collection

The PERCEPTION pipeline

Cross-platform comparison of PERCEPTION performance

PERCEPTION prediction of monotherapy and combination response in a lung cancer cell lines screen

PERCEPTION’s prediction in patient-derived head and neck cancer cell lines

Predicting combinations response in multiple myeloma patients

Predicting combinations response in breast cancer clinical trial analysis

Building bulk-based drug response models to distinguish responders from non-responders

Predicting the development of resistance to multiple tyrosine kinase inhibitors trial in lung cancer patients

Literature survey of cross-resistance and cross-sensitivity

Declarations

Data availability

Code availability

Acknowledgments

Competing interests

References

Additional Declarations

Supplementary Files

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