1. High-throughput genomic profiling of tumor-infiltrating leukocytes
Presented by: Bryan Shaw

2. Background
TIL are a part of tumors and are well know to have major roles in cancer biology
TIL are vital in tumor growth, cancer progression and response to therapy.

3. Figure 1. Current and emerging techniques for evaluating TIL composition in solid tumors.
Aaron M Newman, Ash A Alizadeh
High-throughput genomic profiling of tumor-infiltrating leukocytes
Current Opinion in Immunology, Volume 41, 2016, 77–84

4. In silico approaches for TIL profiling
Enrichment measures for genes associated with individual cell types
Leukocyte have established markers for FACS(Fluorescence-activated cell sorting) or IHC (immunohistochemistry)
Algorithmic deconvolution of admixed transcriptomes to resolve composition
Generating GEP (gene expression profiles)
Derive additional marker genes using in silico nanodissection, a novel machine learning technique for predicting cell type-specific genes from GEP mixture data 

5. Class
Method
Input: Marker genes
Input: Signature matrix
Output: Gene enrichment
Output: Cell type proportions
Robust to >50% unknown content?
Robust to random noise or multi-cell type perturbation?
Marker gene expression obtained from mixture samples?
Resolution of closely related cell types?
Significance analysis for gene enrichment or cell type proportions?
Enrichment methods
Cluster analysis NA ND N
TIL meta-genes Y
SPEC
ssGSEA
Deconvolution methods
DSA
MMAD
CIBERSORT
LLSR
PERT QP

6. Marker gene enrichment
Using gene clusters is not useful for assigning marker genes as they are sensitive to noise
Gene enrichment is not good for discriminating cell subsets but is good for inferring there are some
Single Sample Gene Set Enrichment Analysis uses preranked GSEA to analyze gene sets

7. Gene expression deconvolution
Gene expression deconvolution is an emerging technique analogous to in silico flow cytometry
The deconvolution methods aim to computationally resolve a GEP into its component cell types (virtual tissue dissection)
Expression Deconvolution
Requires a signature matrix consisting of marker genes and their expression values
Also requires a biological mixture
There is also a vector which contains the cell subset of the mixture in the signature matrix
m = G x f

8. Gene expression deconvolution
These methods designed to analyze peripheral blood
Abbas et al created an iterative least squares approach solving for f with a new signature matrix creation strategy
Gong expanded on this work using quadratic programming
Using non-negative constraints (cell fractions >0) able to demonstrate advantages of this method

9. Cell deconvolution for solid tumors
Tumors are heterogeneous mixtures of cell types
Very challenging requires deconvo methods to be extremely robust due to unknown cell types
Digital Sorting Algorithm helped to reduce biological noise
infer the expression levels of user-specified marker genes directly from RNA mixture samples
Similar cell types are able to be given coefficients and grouped together to more accurately reflect their proportions in sample

10. Cell deconvolution for solid tumors
Using CIBERSORT to enumarate cell proportions based on vector regression
Uses linear loss function and L2-norm regulation making it robust to micture content , noise, and highly correlated cell subsets
CIBERSORT was able to reveal complex associations between 22 leukocytes subsets from a GEP of 25 tumor types and thousands of tumor samples

11. Calibration of in silico TIL profiling methods
Establish baseline performance, use marker genes (and signature matrices) be validated by analyzing independently generated GEPs
the same leukocyte phenotypes
different leukocyte phenotypes
samples devoid of immune content
Deconvolution methods should be robust by
resolving all cell types within a given signature matrix; if external datasets are not available, this can be accomplished by leave-one-out cross validation
Comparisons against flow cytometry, expression profiling performed on RNA to avoid alterations in cellular representation

12. Outstanding issues Maximum phenotype distinction is open
Results depend on
Features of the selected algorithm
Robustness of the marker genes
Level of unknown content and/or noise
Expression profiling platform
all methods are expected to benefit from the lower noise levels and increased dynamic range of RNA-seq

13. Outlook
“In silico tissue dissection is an emerging class of techniques for large- scale characterization of tumor cellular heterogeneity…When integrated with complementary data, including somatic and epigenetic alterations, B-cell and T-cell receptor profiles, and imaging, in silico tissue dissection will lead to more comprehensive portraits of human tumors. In the near future, we expect such analyses to transform cancer therapy and management.”