This document summarizes a study that used high throughput transcript profiling to analyze common and cell-type specific responses to anti-cancer drugs across 6 breast cancer cell lines treated with 109 drugs. The researchers used the L1000 assay to generate gene expression signatures from the cell lines in response to drug treatments at different doses and time points. They analyzed the signatures using the characteristic direction method and clustered the signatures based on cosine distance to identify different response patterns among the cell lines. Their results provide insights into variable drug responses in breast cancer subtypes.

1. Common and cell-type specific responses to anti-cancer drugs
revealed by high throughput transcript profiling
Nature Communications 2017
Bioinformatics Journal Club 03/21/ 2018
Thi Nguyen, Ph.D. Candidate
Graduate Biomedical Sciences | Immunology Theme
University of Alabama at Birmingham (UAB)
kimthi@uab.edu

2. Outline
1. Authors
2. Background
• Biomedical concepts
• Rationale and approach
• CMAP and LINCS
3. Methods:
• L1000 assay
• Clustering of Cosine Distance (CD) signature
• Growth Rate Inhibition (GR)
• VIPER analysis of the transcriptional signature
5. Figures
6. Conclusion

3. Authors (1)
Mario Niepel, Ph.D.
• Post-doc at Dept. of System Biology, Harvard Medical School
• Research interest: cell type specific network responses in cancer
• Approaches:
1. Use ODE models and Boolean Logic Networks to model breast cancer responses.
2. Use single cell measurement of signaling events to study
heterogeneity in EGF network.
Marc Hafner, Ph.D.
• Post-doc at Dept. of System Biology, Harvard Medical School
• Research interest: cancer drug resistance, therapeutic design, biomarkers discovery.
• Approaches
1. Develop novel computational methods to quantify phenotypes in biological systems
2. Integrate in vitro-omics data with patients data to understand variable efficacy of
cancer therapies.

4. Authors (2)
Avi Ma’ayan, Ph.D.
• Director of Mount Sinai Center for Bioinformatics
• Professor in Dept. Pharmacological Sciences
• PI of the BD2K-LINCS DCIC and Mount Sinai Knowledge Management
Center for Illuminating the Druggable Genome.
• Ma’ayan Lab applies computational and mathematical methods to
study complexity of regulatory networks in mammalian cells.
Peter Sorger, Ph.D.
• Professor of System Biology at Harvard Medical School
• Head of Harvard Program in therapeutic Sciences
• Director of Laboratory of Systems Pharmacology
• Cofounder of Merrimack Pharmaceuticals and Glencoe Software

5. Biomedical concepts
• Cancer is a genetic disease -> genome
instability -> series of mutations ->
uncontrolled cell growth + invasion +
metastasis
• Types of cancer mutations: proto-oncogene,
tumor-suppressor, DNA repairs and
apoptosis regulatory genes.

6. Genome-based approach to cure cancer?
Cancer genome sequencing
Driver Passenger
Yes
% Druggable target?
no
Cancer mutations
Proto-oncogene Tumor suppressor
Restore function?
Gene therapy Chemical compound
Genetic dependency/
Collateral vulnerabilities
20% 80%

7. Rationale and approach
• For many types of cancer and drugs, there is no simple genetic predictor of responses.
• E.g. genes encoding members of the Akt/PI3K/mTOR pathway are commonly
mutated in breast cancer, but the presence of these mutations is a poor predictor of
responsiveness to inhibitors of Akt/PI3K/mTOR kinases.
• Phenotype: growth rate inhibition assay
• Molecular response: L1000 signatures
APPROACH:
• 6 Breast Cancer Cell lines from 3 major subtypes: HER2amp, HR+, and TNBC and 1 non-
malignant MCF 10A
• 109 drugs
• 6 different doses: 0.04 → 10 μM
• Time: transcript L1000 at 3h and 24h and phenotype GR at 0h and 72h.
6 X 109 x 6 x 2 = ~ 8000
gene expression signatures
6 doses

8. The Connectivity MAP (CMAP)
• Broad Institute, 2006
• gene expression signature to connect drugs, genes and
diseases.
• Drugs: 164 distinct small molecule= perturbagens
• Cells: Breast cancer cell line MCF7, cancer epithelial cell line
PC3, leukemia HL60 and melanoma SKMEL5
• Dose = 10uM
• Time: 6 hours (a smaller subset 12 hours for comparisons).

9. The Connectivity MAP (CMAP) 2006
Lamb J, et al. The Connectivity Map: using gene-expression signatures to connect
small molecules, genes, and disease. Science. 2006/9/29. 313(5795):1929-35, (2006)
Diseases signature Drug signature Connectivity

10. • small molecules
• Gene knockdown/
overexpression
• physiological signals
LINCS: Library of Integrated Network-based Cellular Signature
(2014-2020)
http://lincsproject.org
• Data generation
• Analysis
• Visualization
• Integration
Functional annotation with
existing knowledge
Mission Statement: “To generate coherent, multi-dimensional datasets of perturbation-induced molecular
and cellular signatures that can be integrated and analyzed by computational methods to inform a network
understanding of biological systems in health and disease, thereby facilitating drug and biomarker
development.”
• immortalized cell
lines
•Primary cells
•Stem cells (ESC, iPSC)
•Cells representative
of diseases
Additional dimensions:
• Dose
• Time
• Perturbation kinetics
• Combination of
perturbations
• Genotypic/phenotypic
variation

11. The LINCS Consortium
• NIH common fund program
• Six Data and Signature Generation Center (DSGCs) and one Data Integration and
Coordination Center (DCIC)
1. NeuroLINCS : human brain cells
in health and disease using iPSC
2. MEP-LINCS: microenvironment
effects on cell molecular
networks
3. DToxS: drug toxicity signature
4. LINCS PCCSE: phosphosignaling
and histone modification using
Mass-spec
5. HMS LINCS: Harvard Medical
School LINCS use multiplex/
diverse approaches (Imaging,
ELISA, MS, RNA seq…)
6. Broad Transcriptomics: L1000
technology
BD2K-LINCS DCIC
• harmonize LINCS data with other available resources
• PI: Avi Ma’ayan, Mario Medvedovic and Stephan Schurer
• Awardee Institution: Icahn School of Med at Mount Sinai

12. L1000 assay
• “L1000 is a multiplexed gene expression assay that uses ligation mediated amplification of
RNA sequence specific probes combined with Luminex beads-based detection to generate
expression profiles of 978 genes per sample in a 384 well format.”
• low cost ($1.5/ sample) + reduced dimension
• developed by Broad Institute investigators to massively scale up CMap dataset, funded by
LINCS.
• Measurement of 978 “landmark” genes are applied to inference algorithm (trained on the
Gene Expression Omnibus data) to infer the expression of 11,350 additional genes.
• Simulation shows the reduced representation is able to recapitulate ~ 80% of the transcripts
if measured directly.
• 1000-fold scale-up of CMAP: Subramanian A, et al. A Next Generation Connectivity Map:
L1000 Platform And The First 1,000,000 Profiles. Cell. 2017/12/1. 171(6):1437–1452.

13. Multiplex gene expression assay
Peck, D. et al. A method for high-throughput gene expression signature analysis.
Genome Biol. 7, R61 (2006).
Flow cytometry
Detection
Combine ligation-mediated
amplification with flow-
cytometric detection

14. L1000 vs. RNA-Seq
https://clue.io/connectopedia/why_not_use_rnaseq

15. Common methods to measure differentially gene expression (DEG)
• Fold change: Ratios of gene expression level, no account of variance
• Welsh’s t test: assume Gaussianity
• Significant Analysis of Microarray (SAM): a modification of t-test
• Limma: apply a linear model to each individual gene
• New method: Characteristic Direction (CD) method

16. Analysis of L1000 data
Characteristic direction (CD) method
• Genes do not function in isolation but as
part of a complex network of interactions
• Univariate approach (gene by gene) can
miss some structure in the data
• Multivariate approaches are sensitive to
the curse of dimensionality
R package: https://cran.r-project.org/web/packages/GeoDE/

17. Characteristic direction (CD) method- a geometrical approach to
identify differentially expressed genes (DEG)
Linear
classification
boundary

18. How to calculate CD?
Class conditional density of x Prior probability of class k
class Gene expression
2/ Model the class-conditional density as a multivariate
Gaussian, with mean 𝜇k and covariance 𝛴k
Linear discriminant analysis
1/Bayes rule for classification probability
𝜇red
𝜇black
𝛴red
𝛴black
3/ Estimate classification probability:
4/ The orientation of the separating hyperplane
b
This calculation involves the inverse of a very large p × p matrix which is not only expensive to
compute but also the elements must be estimated from a relatively small sample-size.

19. How to interpret CD?
c1
c2
M
M2 = c1
2 + c2
2
M2 = c1
2 + c2
2 + c3
2 +…+ cn
2
total differential M = sum of square of components
What it means:
The more aligned the CD with an axis of the
particular gene, the more significant that gene is
in the differential expression.

20. CD method:
Address the curse of dimensionality
• The estimate of the covariance matrix is likely to be fraught with error because of
the curse of dimensionality
• Use shrinkage/ regularization = smooth away error while retaining signal
• Shrink covariance matrix to the scalar variance
• Where Ip is the p x p identity matrix
• When 𝛾 = 1, it gives the original covariance matrix
• When 𝛾 = 0, it results in a diagonal covariance estimate with each value being shrunk
towards the scalar variance 𝜎2
• Intermediate value 𝛾 result in a mixture of the two.
• The inclusion of a constant on the diagonal resolves the singularity problem, and the
modulation of the off-diagonal terms helps to reduce noise arising from the estimation
of covariance from few samples.

21. L1000 data analysis pipeline
L1000 data is provided at five levels in the data processing pipeline:
● Level 1: Raw unprocessed flow cytometry data from Luminex (LXB)
● Level 2: Gene expression values per 1000 genes after deconvolution (GEX)
● Level 3: Quantile-normalized gene expression profiles of landmark genes and imputed
transcripts (QNORM or INF)
● Level 4: Gene signatures computed using z-scores relative to the plate population as control
(ZSPCINF) or relative to the plate vehicle control(ZSVCINF)
● Level 5: Differential gene expression signatures

22. L1000 data analysis
• CD was calculated per batch
• Batch = group of experimental conditions at the same time, cell line but on multiple plates
• M, the number of experimental conditions.
• N, the number of control replicates.
• J, the number of plates.
• Xi,j, a vector of length 978 representing the jth replicate of the ith experimental condition.
• Cj,k, a vector of length 978 representing the kth control replicate on the jth plate.
Ø First, we calculate the CD for each experimental condition J times, each times using a
replicate Xi,j and the controls from the same plate to obtain Di,j = f(Cj, Xi,j) where f is the CD
function and Cj is the control matrix.
Ø Final CD, Di for an experimental condition is:

23. Signature Consistency Score (SCS)
Ø Estimate the significance of the CD:
• Null hypothesis: variation of CD between technical replicate is equal to that of a randomly
selected samples from different cell line and treatments (equal number of perturbations)
• To calculate null distribution, we define Si as the mean of all possible pair-wise cosine
distance between Di,j of the ith experimental condition:
• To estimate the null distribution of Si, we randomly drew J number of Di,j from the
pool of M·J conditions and calculated their average cosine distance as Sn.
• We repeated the process for 10,000 times to obtain the null empirical distribution.
Ø The Signature Consistency Score (SCS): assess the degree of alignment of CD vectors for
replicates relative to randomly chosen vectors.
• SCS is the negative log of the one-tail comparison (on the lower end) of Si with the null
distribution Sn.
• SCS is effective to quantify the reliability of a transcriptional response normalized by
background experimental noise.

24. Clustering of CD signatures
• Based on cosine distance between the CD signature with SCS > 1.3
• Relies on the fuzzy c-means algorithm (MATHLAB: fcm, with an exponent for
the membership function matrix of 1.22)
• fuzzy clustering allows genes to belong to more than one cluster -> allow
for the identification of genes that are conditionally co-regulated or co-
expressed.

25. Fuzzy C-means clustering
• Is a clustering algorithm where each item may belong to more than one group, where the degree of
membership for each item is given by a probability distribution over the clusters.
• Iterations: In each iteration of the FCM algorithm, the objective function J is minimized:
N is the number of data points, C is the number of clusters, cj is the centre vector for cluster j , and 𝛿ij is
the degree of membership for the ith data point xi . The norm, xi – ci measures the closeness of the data
point xi to the centre vector cj
• Note: in each iteration, the algorithm maintains a centre vector for each of the clusters.
These data-points are calculated as the weighted average of the data-points, where the weights
are given by the degrees of membership:
Where m is the fuzziness coefficient and the centre vector ci is calculated:
• m determines how much the cluster can overlap with each other 1 < m < ∞

26. Benchmark fuzzy clustering
Sup. Fig. 8: Average results for 8 independent clustering runs with different parameters
Consistency of
clustering across
multiple repeats.
Fraction of
perturbation in a
cluster
Number of empty
clusters

27. Growth rate inhibition (GR)
a new metric to study drug response
• Compensate for confounding effects of
division rates on drug response
measurements
• division rate varies with cell type, media
composition and seeding density, often
in unpredictable ways
• GR = 0 : cytostasis
• 0 < GR <1 : partial growth inhibition
• -1 <GR<0 : cell death

28. Assessing drug synergy
• Bliss independence model to measure drug addivity:
• i1, i2, i12 are inhibitions as a fraction (fractional inhibitions) by drug1, drug2 and their
combination
• Assuming drug1 and drug2 have independent actions, when fraction i1 is inhibited by drug1,
only (1 - i1) are left to respond to drug2, then: i12 = i1 + i2(1 - i1) = i1 + i2 - i1i2
• Can be rewritten in terms of “the unaffected fraction”: u = 1 - i
• Excess over bliss: EOB = observed inhibition – expected inhibition:
• EOB = 0: additive effect
• EOB > 0: synergistic effect
• EOB < 0: antagonistic effect

29. VIPER analysis of the transcriptional signature
• VIPER = virtual inference of protein activity by enriched regulon analysis:
• Regulon = expression of the transcriptional targets of a protein
• ARACNE algorithm is used to reconstruct gene regulatory network
• ARACNE uses mutual information to quantify the interaction between genes, and then
removes the vast majority of indirect candidate interactions using a well-known
information theoretic property, the data processing inequality (DPI):
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810318/
https://www.nature.com/articles/ng.3593

30. Key questions
1. Does same phenotypic responses (cytostasis or death) to a
particular drug mean the same transcriptional responses?
2. Can there be a change in transcriptional response without a
change in phenotypic response?

31. • Color -> cluster identity
• Size -> concentration
• Shapes -> time point
• Each node -> unique
perturbation combination
• Only include 2864 out of 7825
drug-cell pairs (SCS > 1.3)
• Cluster is made based on
cosine distance of CD (full
Transcriptome)
• Black dots; perturbations
that are <55% membership
of any other clusters.
Fig.1. L1000 signatures of drug responses

32. Sup. Fig.1. CD clusters of drug responses
17-18-19-20
Ø Pattern 1: cluster of cell-cycle/ chaperone inhibitor
• Cluster 13 (3h) -> Cluster 12 (24h)
• Cluster 19/20 (3h) -> Cluster 17/18 (24h)
• GSE analysis shows MAPK/GSK3b signaling was
down at 3h and CDK/mitosis signaling was down at
24h.
• Same GR phenotype
Ø Pattern 2: cluster of the same cell lines treated with
kinase inhibitors.
Ø Cluster 2-11
Ø GR phenotypes vary

33. Fig.2a. Variation in phenotypic response
≠ Cell lines

34. • As dose is increased, CD magnitude is also weakly correlated, spearman 0.13 < 𝛒 < 0.32
• Angle of CD vectors change modestly.
Fig 2b + sup.Fig.2b. Effect of doses on molecular responses
Sup.Fig.2b
Fig.2b
• Greatest CD cosine distance is seen with ECM and RTK inhibitors
• Cluster 14, 15, 17 is 10uM

35. Fig.3. Compare variation in L1000 signature and phenotypic response
with drug class
• GRAOC : a measure of
phenotypic response across
all doses for a particular
drug (capture both potency
+ efficacy)
• GRAOC is calculated by
integrating the GR curve
over a range of
concentrations
24h
Shortgun MS
phosphoproteomics

36. Rationalize response to RTK inhibitors?
• High RTK abundance is necessary for sensitivity to RTK inhibitors, e.g.
Hs578T expresses high level of PDGF-R and MCF-7 express IGF1R
• High RTK level doesn’t mean sensitivity to RTK inhibition at phenotype level,
e.g. BT-20 has high ErbB1 levels.
Sup2a: Dose response curves across six cell lines for a subset of drugs that exhibit
significant responses at the level of L1000 signature and phenotype

37. Fig.4. Transcriptional responses generally correlates with
phenotypic responses

38. Fig.5. Class IV drugs are synergistic with drugs targeting
the PI3K pathway
• BT-20 cells have an activating mutation in the kinase domain of PI3K𝛼
• Modest response to PI3K inhibitor
• Suspect other RTK like MAPK may rescue them and vice versa
• Nodes = unique perturbation
• Node size = dose
• Edge = drawn between perturbation with cosine
distance in lower 5 percentile
• Color = drugs

39. Strategies to find synergistic drugs
1. “Orthogonal” approach:
• Target different pathways/ clusters to combat against adaptive resistance
E.g. in BT-20 cells: combination of drugs from distinct clusters: PI3K/AKT inhibitors
and EGFR/MAPK inhibitors.
2. “Non-orthogonal” approach:
• Inhibit same protein or pathway with multiple drugs to achieve better target
coverage
E.g. Hs 578T cells, synergy was achieved with drugs in the same cluster (neratinib
and foretinib)

40. Fig.6. FoxO3a signaling as the mechanism of drug synergy
Fig.6. Distribution of the nuclear/cytoplasmic ratio FoxO3a measured by quantitative
immunofluorescence microscopy.

41. Summary of Findings
1. Drug responses at the transcript level are well correlated with those at phenotype level
2. CDK or chaperone inhibitor induced similar changes across cell lines
3. Signaling inhibitors induced cell-line-specific changes.
4. RTK inhibitors induced highly variable responses
5. Some drug/cell line pairs have significant change in transcription but no change in cell
growth.
6. Identify drug synergy by orthogonal or non-orthogonal approach.
7. Classify equivalence drugs based on co-clustering effect to support drug substitutions.