Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction.

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

Evolution of chromosome-arm aberrations in breast cancer through genetic network rewiring.

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

SON is an essential m6A target for hematopoietic stem cell fate.

Stem cells regulate their self-renewal and differentiation fate outcomes through both symmetric and asymmetric divisions. m6A RNA methylation controls symmetric commitment and inflammation of hematopoietic stem cells (HSCs) through unknown mechanisms. Here, we demonstrate that the nuclear speckle protein SON is an essential m6A target required for murine HSC self-renewal, symmetric commitment, and inflammation control. Global profiling of m6A identified that m6A mRNA methylation of Son increases during HSC commitment. Upon m6A depletion, Son mRNA increases, but its protein is depleted. Reintroduction of SON rescues defects in HSC symmetric commitment divisions and engraftment. Conversely, Son deletion results in a loss of HSC fitness, while overexpression of SON improves mouse and human HSC engraftment potential by increasing quiescence. Mechanistically, we found that SON rescues MYC and suppresses the METTL3-HSC inflammatory gene expression program, including CCL5, through transcriptional regulation. Thus, our findings define a m6A-SON-CCL5 axis that controls inflammation and HSC fate.

Uncovering a mammalian neural-specific poly(A) binding protein with unique properties.

The mRNA 3' poly(A) tail plays a critical role in regulating both mRNA translation and turnover. It is bound by the cytoplasmic poly(A) binding protein (PABPC), an evolutionarily conserved protein that can interact with translation factors and mRNA decay machineries to regulate gene expression. Mammalian PABPC1, the prototypical PABPC, is expressed in most tissues and interacts with eukaryotic translation initiation factor 4G (eIF4G) to stimulate translation in specific contexts. In this study, we uncovered a new mammalian PABPC, which we named neural PABP (neuPABP), as it is predominantly expressed in the brain. neuPABP maintains a unique architecture as compared with other PABPCs, containing only two RNA recognition motifs (RRMs) and maintaining a unique N-terminal domain of unknown function. neuPABP expression is activated in neurons as they mature during synaptogenesis, where neuPABP localizes to the soma and postsynaptic densities. neuPABP interacts with the noncoding RNA BC1, as well as mRNAs coding for ribosomal and mitochondrial proteins. However, in contrast to PABPC1, neuPABP does not associate with actively translating mRNAs in the brain. In keeping with this, we show that neuPABP has evolved such that it does not bind eIF4G and as a result fails to support protein synthesis in vitro. Taken together, these results indicate that mammals have expanded their PABPC repertoire in the brain and propose that neuPABP may support the translational repression of select mRNAs.

Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows has enabled clinically-relevant tumor type predictions to guide treatment decisions in real time.

RNA-binding proteins that lack canonical RNA-binding domains are rarely sequence-specific.

Thousands of RNA-binding proteins (RBPs) crosslink to cellular mRNA. Among these are numerous unconventional RBPs (ucRBPs)-proteins that associate with RNA but lack known RNA-binding domains (RBDs). The vast majority of ucRBPs have uncharacterized RNA-binding specificities. We analyzed 492 human ucRBPs for intrinsic RNA-binding in vitro and identified 23 that bind specific RNA sequences. Most (17/23), including 8 ribosomal proteins, were previously associated with RNA-related function. We identified the RBDs responsible for sequence-specific RNA-binding for several of these 23 ucRBPs and surveyed whether corresponding domains from homologous proteins also display RNA sequence specificity. CCHC-zf domains from seven human proteins recognized specific RNA motifs, indicating that this is a major class of RBD. For Nudix, HABP4, TPR, RanBP2-zf, and L7Ae domains, however, only isolated members or closely related homologs yielded motifs, consistent with RNA-binding as a derived function. The lack of sequence specificity for most ucRBPs is striking, and we suggest that many may function analogously to chromatin factors, which often crosslink efficiently to cellular DNA, presumably via indirect recruitment. Finally, we show that ucRBPs tend to be highly abundant proteins and suggest their identification in RNA interactome capture studies could also result from weak nonspecific interactions with RNA.

A deep learning approach reveals unexplored landscape of viral expression in cancer.

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.

Regional mutational signature activities in cancer genomes.

Cancer genomes harbor a catalog of somatic mutations. The type and genomic context of these mutations depend on their causes and allow their attribution to particular mutational signatures. Previous work has shown that mutational signature activities change over the course of tumor development, but investigations of genomic region variability in mutational signatures have been limited. Here, we expand upon this work by constructing regional profiles of mutational signature activities over 2,203 whole genomes across 25 tumor types, using data aggregated by the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium. We present GenomeTrackSig as an extension to the TrackSig R package to construct regional signature profiles using optimal segmentation and the expectation-maximization (EM) algorithm. We find that 426 genomes from 20 tumor types display at least one change in mutational signature activities (changepoint), and 306 genomes contain at least one of 54 recurrent changepoints shared by seven or more genomes of the same tumor type. Five recurrent changepoint locations are shared by multiple tumor types. Within these regions, the particular signature changes are often consistent across samples of the same type and some, but not all, are characterized by signatures associated with subclonal expansion. The changepoints we found cannot strictly be explained by gene density, mutation density, or cell-of-origin chromatin state. We hypothesize that they reflect a confluence of factors including evolutionary timing of mutational processes, regional differences in somatic mutation rate, large-scale changes in chromatin state that may be tissue type-specific, and changes in chromatin accessibility during subclonal expansion. These results provide insight into the regional effects of DNA damage and repair processes, and may help us localize genomic and epigenomic changes that occur during cancer development.

PAN-cODE: COVID-19 forecasting using conditional latent ODEs.

The coronavirus disease 2019 (COVID-19) pandemic has caused millions of deaths around the world and revealed the need for data-driven models of pandemic spread. Accurate pandemic caseload forecasting allows informed policy decisions on the adoption of non-pharmaceutical interventions (NPIs) to reduce disease transmission. Using COVID-19 as an example, we present Pandemic conditional Ordinary Differential Equation (PAN-cODE), a deep learning method to forecast daily increases in pandemic infections and deaths. By using a deep conditional latent variable model, PAN-cODE can generate alternative caseload trajectories based on alternate adoptions of NPIs, allowing stakeholders to make policy decisions in an informed manner. PAN-cODE also allows caseload estimation for regions that are unseen during model training. We demonstrate that, despite using less detailed data and having fully automated training, PAN-cODE's performance is comparable to state-of-the-art methods on 4-week-ahead and 6-week-ahead forecasting. Finally, we highlight the ability of PAN-cODE to generate realistic alternative outcome trajectories on select US regions.

Reconstructing cancer phylogenies using Pairtree, a clone tree reconstruction algorithm.

Pairtree is a clone tree reconstruction algorithm that uses somatic point mutations to build clone trees describing the evolutionary history of individual cancers. Using the Pairtree software package, we describe steps to preprocess somatic mutation data, cluster mutations into subclones, search for clone trees, and visualize clone trees. Pairtree builds clone trees using up to 100 samples from a single cancer with at least 30 subclonal populations. For complete details on the use and execution of this protocol, please refer to Wintersinger et al. (2022).

PRIESSTESS: interpretable, high-performing models of the sequence and structure preferences of RNA-binding proteins.

Modelling both primary sequence and secondary structure preferences for RNA binding proteins (RBPs) remains an ongoing challenge. Current models use varied RNA structure representations and can be difficult to interpret and evaluate. To address these issues, we present a universal RNA motif-finding/scanning strategy, termed PRIESSTESS (Predictive RBP-RNA InterpretablE Sequence-Structure moTif regrESSion), that can be applied to diverse RNA binding datasets. PRIESSTESS identifies dozens of enriched RNA sequence and/or structure motifs that are subsequently reduced to a set of core motifs by logistic regression with LASSO regularization. Importantly, these core motifs are easily visualized and interpreted, and provide a measure of RBP secondary structure specificity. We used PRIESSTESS to interrogate new HTR-SELEX data for 23 RBPs with diverse RNA binding modes and captured known primary sequence and secondary structure preferences for each. Moreover, when applying PRIESSTESS to 144 RBPs across 202 RNA binding datasets, 75% showed an RNA secondary structure preference but only 10% had a preference besides unpaired bases, suggesting that most RBPs simply recognize the accessibility of primary sequences.

Activating mTOR Mutations Are Detrimental in Nutrient-Poor Conditions.

The mTOR is a key regulator of cell growth that integrates growth factor signaling and nutrient availability and is a downstream effector of oncogenic receptor tyrosine kinases (RTK) and PI3K/Akt signaling. Thus, activating mTOR mutations would be expected to enhance growth in many tumor types. However, tumor sequencing data have shown that mTOR mutations are enriched only in renal clear cell carcinoma, a clinically hypervascular tumor unlikely to be constrained by nutrient availability. To further define this cancer-type-specific restriction, we studied activating mutations in mTOR. All mTOR mutants tested enhanced growth in a cell-type agnostic manner under nutrient-replete conditions but were detrimental to cell survival in nutrient-poor conditions. Consistently, analysis of tumor data demonstrated that oncogenic mutations in the nutrient-sensing arm of the mTOR pathway display a similar phenotype and were exceedingly rare in human cancers of all types. Together, these data suggest that maintaining the ability to turn off mTOR signaling in response to changing nutrient availability is retained in most naturally occurring tumors. SIGNIFICANCE: This study suggests that cells need to inactivate mTOR to survive nutrient stress, which could explain the rarity of mTOR mutations and the limited clinical activity of mTOR inhibitors in cancer.

Prognostic impact of autoimmune disease in oral cavity squamous cell carcinoma.

BACKGROUND AND OBJECTIVES: Recent literature has highlighted the role of the host in the prognosis of oral squamous cell carcinoma (OSCC). In this study, we retrospectively examined the impact of autoimmune (AI) disorders as an aspect of the host status on survival outcomes in OSCC patients. METHODS: From a departmental database of OSCC patients (n = 1369), 123 patients with an AI disorder were identified. AI and no-AI groups were compared for survival outcomes. RESULTS: There were no significant differences in survival between groups for overall survival, disease-specific survival, local, regional, and distant recurrence-free probabilities. However, survival and recurrence-free probabilities were poorer in the AI group versus the no AI group. CONCLUSION: Patients with AI disease trended towards worse outcomes. This suggests immune dysregulation in these patients may impact oncologic outcomes.

The X-linked splicing regulator MBNL3 has been co-opted to restrict placental growth in eutherians.

Understanding the regulatory interactions that control gene expression during the development of novel tissues is a key goal of evolutionary developmental biology. Here, we show that Mbnl3 has undergone a striking process of evolutionary specialization in eutherian mammals resulting in the emergence of a novel placental function for the gene. Mbnl3 belongs to a family of RNA-binding proteins whose members regulate multiple aspects of RNA metabolism. We find that, in eutherians, while both Mbnl3 and its paralog Mbnl2 are strongly expressed in placenta, Mbnl3 expression has been lost from nonplacental tissues in association with the evolution of a novel promoter. Moreover, Mbnl3 has undergone accelerated protein sequence evolution leading to changes in its RNA-binding specificities and cellular localization. While Mbnl2 and Mbnl3 share partially redundant roles in regulating alternative splicing, polyadenylation site usage and, in turn, placenta maturation, Mbnl3 has also acquired novel biological functions. Specifically, Mbnl3 knockout (M3KO) alone results in increased placental growth associated with higher Myc expression. Furthermore, Mbnl3 loss increases fetal resource allocation during limiting conditions, suggesting that location of Mbnl3 on the X chromosome has led to its role in limiting placental growth, favoring the maternal side of the parental genetic conflict.

Reconstructing Complex Cancer Evolutionary Histories from Multiple Bulk DNA Samples Using Pairtree.

Cancers are composed of genetically distinct subpopulations of malignant cells. DNA-sequencing data can be used to determine the somatic point mutations specific to each population and build clone trees describing the evolutionary relationships between them. These clone trees can reveal critical points in disease development and inform treatment. Pairtree is a new method that constructs more accurate and detailed clone trees than previously possible using variant allele frequency data from one or more bulk cancer samples. It does so by first building a Pairs Tensor that captures the evolutionary relationships between pairs of subpopulations, and then it uses these relations to constrain clone trees and infer violations of the infinite sites assumption. Pairtree can accurately build clone trees using up to 100 samples per cancer that contain 30 or more subclonal populations. On 14 B-progenitor acute lymphoblastic leukemias, Pairtree replicates or improves upon expert-derived clone tree reconstructions. SIGNIFICANCE: Clone trees illustrate the evolutionary history of a cancer and can provide insights into how the disease changed through time (e.g., between diagnosis and relapse). Pairtree uses DNA-sequencing data from many samples of the same cancer to build more detailed and accurate clone trees than previously possible. See related commentary by Miller, p. 176. This article is highlighted in the In This Issue feature, p. 171.

Multiomic Profiling of Central Nervous System Leukemia Identifies mRNA Translation as a Therapeutic Target.

Central nervous system (CNS) dissemination of B-precursor acute lymphoblastic leukemia (B-ALL) has poor prognosis and remains a therapeutic challenge. Here we performed targeted DNA sequencing as well as transcriptional and proteomic profiling of paired leukemia-infiltrating cells in the bone marrow (BM) and CNS of xenografts. Genes governing mRNA translation were upregulated in CNS leukemia, and subclonal genetic profiling confirmed this in both BM-concordant and BM-discordant CNS mutational populations. CNS leukemia cells were exquisitely sensitive to the translation inhibitor omacetaxine mepesuccinate, which reduced xenograft leptomeningeal disease burden. Proteomics demonstrated greater abundance of secreted proteins in CNS-infiltrating cells, including complement component 3 (C3), and drug targeting of C3 influenced CNS disease in xenografts. CNS-infiltrating cells also exhibited selection for stemness traits and metabolic reprogramming. Overall, our study identifies targeting of mRNA translation as a potential therapeutic approach for B-ALL leptomeningeal disease. SIGNIFICANCE: Cancer metastases are often driven by distinct subclones with unique biological properties. Here we show that in B-ALL CNS disease, the leptomeningeal environment selects for cells with unique functional dependencies. Pharmacologic inhibition of mRNA translation signaling treats CNS disease and offers a new therapeutic approach for this condition.This article is highlighted in the In This Issue feature, p. 1.