Adaptive digital tissue deconvolution.

MOTIVATION: The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g. a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions. RESULTS: We propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. AVAILABILITY AND IMPLEMENTATION: A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362).

Assessment of Variables Related to the Risk of Severe Adverse Events in Cutaneous Melanoma Patients Treated with Immune Checkpoint Inhibitors.

Malignant melanoma is a prevalent and aggressive cancer, with globally increasing incidences. While immune checkpoint inhibitors (ICIs) have prolonged the survival of patients with advanced melanoma over the last decade, this improvement comes with the risk of severe immune-related adverse events (irAEs). This systematic review investigates patient baseline characteristics (BCs) as predictive factors for developing severe gastrointestinal, hepatic, and pulmonary irAEs in patients treated with ipilimumab (anti-CTLA-4) and/or nivolumab/pembrolizumab (anti-PD-1). A systematic literature search was conducted in the Ovid databases MEDLINE and EMBASE on 22 April 2022, following the PRISMA guidelines. Out of 1694 articles, 13 were included in the final analysis. We analyzed BCs and the occurrence of severe colitis, hepatitis, and pneumonitis in 22 treatment arms and 3 treatment groups: anti-CTLA-4 (n = 2904), anti-PD-1 (n = 1301), or combination therapy (n = 822). However, missing data preclude a direct comparison of individual BCs and the association to specific irAEs between studies. Descriptive analysis did not identify any significant association between median age, gender distribution, or performance status and severe colitis, hepatitis, or pneumonitis for any of the three treatment groups. We call for greater transparency and standardization in the reporting of patient-specific irAEs.

Loss-Function Learning for Digital Tissue Deconvolution.

The gene expression profile of a tissue averages the expression profiles of all cells in this tissue. Digital tissue deconvolution addresses the following inverse problem: given the expression profile y of a tissue, what is the cellular composition c of that tissue? If X is a matrix whose columns are reference profiles of individual cell types, the composition c can be computed by minimizing ℒ ( y - X c ) for a given loss function ℒ . Current methods use predefined all-purpose loss functions. They successfully quantify the dominating cells of a tissue, while often falling short in detecting small cell populations. In this study we use training data to learn the loss function ℒ along with the composition c . This allows us to adapt to application-specific requirements such as focusing on small cell populations or distinguishing phenotypically similar cell populations. Our method quantifies large cell fractions as accurately as existing methods and significantly improves the detection of small cell populations and the distinction of similar cell types.

DTD: An R Package for Digital Tissue Deconvolution.

Digital tissue deconvolution (DTD) estimates the cellular composition of a tissue from its bulk gene-expression profile. For this, DTD approximates the bulk as a mixture of cell-specific expression profiles. Different tissues have different cellular compositions, with cells in different activation states, and embedded in different environments. Consequently, DTD can profit from tailoring the deconvolution model to a specific tissue context. Loss-function learning adapts DTD to a specific tissue context, such as the deconvolution of blood, or a specific type of tumor tissue. We provide software for loss-function learning, for its validation and visualization, and for applying the DTD models to new data.

Principles of Systems Biology, No. 31.

This month: selected work from the 2018 RECOMB meeting, organized by Ecole Polytechnique and held last April in Paris.