Dataset Information

Identification of conserved evolutionary trajectories in tumors.

ABSTRACT:

Motivation

As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics).

Results

In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories.

Availability and implementation

CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Hodzic E

PROVIDER: S-EPMC7355238 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Identification of conserved evolutionary trajectories in tumors.

Hodzic Ermin E Shrestha Raunak R Malikic Salem S Collins Colin C CC Litchfield Kevin K Turajlic Samra S Sahinalp S Cenk SC

Bioinformatics (Oxford, England) 20200701 Suppl_1

<h4>Motivation</h4>As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with sim ...[more]

PMID: 32657374

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Project description:BackgroundThe efficacy of immune checkpoint blockade (ICB) depends on restoring immune recognition of cancer cells that have evaded immune surveillance. Transforming growth factor-beta (TGFβ) is associated with immune-poor, so-called cold tumors whereas loss of its signaling promotes DNA misrepair that could stimulate immune response.MethodsWe analyzed transcriptomic data from IMvigor210, The Cancer Genome Atlas, and Tumor Immune Syngeneic MOuse data sets to evaluate the predictive value of high βAlt, a score representing low expression of a signature consisting of TGFβ targets and high expression of genes involved in error-prone DNA repair. The immune context of βAlt was assessed by evaluating tumor-educated immune signatures. An ICB-resistant, high βAlt preclinical tumor model was treated with a TGFβ inhibitor, radiation, and/or ICB and assessed for immune composition and tumor control.ResultsWe found that a high βAlt score predicts ICB response yet is paradoxically associated with an immune-poor tumor microenvironmentcancer in both human and mouse tumors. We postulated that high βAlt cancers consist of cancer cells in which loss of TGFβ signaling generates a TGFβ rich, immunosuppressive tumor microenvironment. Accordingly, preclinical modeling showed that TGFβ inhibition followed by radiotherapy could convert an immune-poor, high βAlt tumor to an immune-rich, ICB-responsive tumor. Mechanistically, TGFβ inhibition increased activated natural killer (NK) cells, which were required to recruit lymphocytes to respond to ICB in irradiated tumors. NK cell activation signatures were also increased in high βAlt, cold mouse and human tumors that responded to ICB.ConclusionsThese studies indicate that loss of TGFβ signaling competency and gain of error-prone DNA repair identifies a subset of cold tumors that are responsive to ICB. Our mechanistic studies show that inhibiting TGFβ activity can convert a high βAlt, cold tumor into ICB-responsive tumors via NK cells. A biomarker consisting of combined TGFβ, DNA repair, and immune context signatures is a means to prospectively identify patients whose cancers may be converted from cold to hot with appropriate therapy.

Dataset Information

Identification of conserved evolutionary trajectories in tumors.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Identification of conserved evolutionary trajectories in tumors.

Similar Datasets

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