Clonal Hematopoiesis Is Driven By Aberrant Activation of TCL1A

Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) may occur when a hematopoietic stem cell (HSC) acquires a fitness-increasing mutation resulting in its clonal expansion. A diverse set of driver genes, such as regulators of DNA methylation, splicing, and chromatin remodeling, have been associated with CHIP, but it remains largely unknown why HSCs bearing these mutations are positively selected. It has been challenging to identify the genetic and environmental factors mediating clonal expansion in humans, partially due to a lack of large cohorts with longitudinal blood sampling of participants. To circumvent this limitation, we developed a method to infer clonal expansion rate from single timepoint data called PACER (passenger-approximated clonal expansion rate). Methods: PACER is based on the principle that genomic passenger mutations can be used to infer the birth date of pre-malignant clones because these mutations accumulate fairly linearly over time. Thus, an individual with CHIP with a greater number of passenger mutations in the mutant clone is expected to have acquired the clone at a later age than someone with fewer passenger mutations. For two individuals of the same age and with clones of the same size, we expect the clone with more passengers to be more fit, as it expanded to the same size in less time. Typically, one would need to isolate single-cell colonies derived from HSCs in order to calculate the total passenger mutation burden. However, we hypothesized that this measure could also be approximated from whole genome sequencing of blood cell DNA, such as that used in large biobank projects. The expansion rate (PACER) is then estimated by adjusting the total passenger count for age and variant allele fraction in each individual. The ability of passengers to predict future clonal expansion was validated using longitudinal blood samples from 51 CHIP carriers in the Women's Health Initiative taken ~10 years apart (Figure 1). It also accurately predicted the known fitness effects due to different driver mutations in 5,551 CHIP carriers from the Trans-Omics for Precision Medicine (TOPMed) program (Figure 2). Results: Having validated the approach, we next hypothesized that we could identify germline variants influencing PACER, thus revealing genes and pathways mediating clonal expansion. The lead hit in a genome-wide association study (GWAS) of PACER was a common single nucleotide polymorphism (SNP) in the TCL1A promoter that was associated with slower clonal expansion in CHIP overall (Figure 3). TCL1A is an oncogene that is activated via translocation in T-cell prolymphocytic leukemia, but has no known role in CHIP or myeloid malignancies. A gene-level analysis indicated that the TCL1A SNP was associated with slower growth of clones bearing TET2 mutations, but had no effect on DNMT3A-mutant clone growth. We further found that those carrying two copies of the protective SNP had 40-80% reduced odds of having clones with driver mutations in TET2, ASXL1, SF3B1, SRSF2, and JAK2, but not DNMT3A. A concomitant decrease in incident myeloid malignancies was also seen in carriers of this protective SNP. Next, we interrogated how the protective SNP influenced TCL1A activity in HSCs. Normal human HSCs lacked open chromatin at the TCL1A promoter and TCL1A expression, but inducing frameshift mutations in TET2 via CRISPR editing led to accessibility of the promoter and gene/protein expression in HSCs (Figure 4). This effect was abrogated in HSCs from donors of the protective TCL1A SNP in a dose-dependent manner. Finally, we found that HSCs from donors homozygous for the protective SNP had markedly less expansion of phenotypic stem and progenitor cells in vitro after the introduction of TET2 mutations than TET2-edited HSCs from donors with two copies of the reference allele. Conclusions: In summary, we developed a novel method to infer the expansion rate of pre-malignant clones and performed the first ever GWAS for this trait. Our results indicate that the fitness advantage of several common driver genes in CHIP and hematological cancers is mediated through TCL1A activation, which may be a therapeutic target to treat these conditions. PACER is an approach that can be widely adopted to uncover genetic and environmental determinants of pre-malignant clonal expansion in blood and other tissues. Figure 1 Figure 1. Disclosures Desai: Bristol Myers Squibb: Consultancy; Kura Oncology: Consultancy; Agios: Consultancy; Astex: Research Funding; Takeda: Consultancy; Janssen R&D: Research Funding. Natarajan: Blackstone Life Sciences: Consultancy; Boston Scientific: Research Funding; Novartis: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; Apple: Consultancy, Research Funding; Amgen: Research Funding; Genentech: Consultancy; Foresite Labs: Consultancy. Jaiswal: Novartis: Consultancy, Honoraria; AVRO Bio: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; Foresite Labs: Consultancy; Caylo: Current holder of stock options in a privately-held company.

Moran Process Version of the Tug-of-War Model: Complex Behavior Revealed by Mathematical Analysis and Simulation Studies

In a series of publications McFarland and co-authors introduced the tug-of-warmodel of evolution of cancer cell populations. The model is explaining the joint effect ofrare advantageous and frequent slightly deleterious mutations, which may be identifiable withdriver and passenger mutations in cancer. In this paper, we put the Tug-of-War model inthe framework of a denumerable-type Moran process and use mathematics and simulationsto understand its behavior. The model is associated with a time-continuous Markov Chain(MC), with a generator that can be split into a sum of the drift and selection process partand of the mutation process part. Operator semigroup theory is then employed to prove thatthe MC does not explode, as well as to characterize a strong-drift limit version of the MCwhich displays instant fixation effect, which was an assumption in the original McFarlandsmodel. Mathematical results are fully confirmed by simulations of the complete and limitversions. They also visualize complex stochastic transients and genealogies of clones arising inthe model.

Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood

Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy.

Review of applications of CRISPR-Cas9 gene-editing technology in cancer research

Characterized by multiple complex mutations, including activation by oncogenes and inhibition by tumor suppressors, cancer is one of the leading causes of death. Application of CRISPR-Cas9 gene-editing technology in cancer research has aroused great interest, promoting the exploration of the molecular mechanism of cancer progression and development of precise therapy. CRISPR-Cas9 gene-editing technology provides a solid basis for identifying driver and passenger mutations in cancer genomes, which is of great value in genetic screening and for developing cancer models and treatments. This article reviews the current applications of CRISPR-Cas9 gene-editing technology in various cancer studies, the challenges faced, and the existing solutions, highlighting the potential of this technology for cancer treatment.

PMC: A more precise classifier of POLE mutations to identify candidates for immune therapy.

3548 Background: Specific somatic mutations in DNA polymerase epsilon ( POLE) can cause a hypermutant phenotype with tumor mutation burden (TMB) in excess of 100 mutations per megabase. It has been reported that POLE mutant tumors are enriched in response to immune therapy and this association is being tested in multiple active clinical trials. However, most POLE mutations are passenger mutations and have no pathogenic role. Current methods to classify POLE mutations are limited in both accuracy and completeness, which could lead to inappropriate use of immune agents in tumor such as MSS CRC, where response rate is 5% or less. Here we present a new classifier, POLE Mutation Classifier or PMC, based on the unique trinucleotide mutation signature caused by selective loss of the proofreading function (LOP) of POLE. Methods: cBioPortal was queried to identify all tumors with POLE mutation. TMB was calculated for each, additionally, trinucleotide mutation signatures were obtained for all POLE mutant tumors in TCGA. Using OncoKB to identify a gold standard of 12 functional POLE mutations (n = 98 tumors) a POLE mutational signature was created. A combination of mutational signature, amino acid location, and TMB was used to classify each POLE variant. Results: Among all 48035 unique tumors the overall frequency of POLE mutations was 2.5% (n = 1184), however only 9.2% (n = 110) were determined to cause the selective LOP. The incidence of LOP POLE mutation was highest in uterine carcinoma and CRC, these tumors also had the highest ratio of LOP to passenger mutations. In a pan-cancer analysis the overall survival of LOP POLE patients was significantly better than those with passenger mutations (not-yet-reached vs. 51 mo, HR = 4.4, p < 0.0001). A similar analysis performed using the polyphen-2 classifier to identify functional POLE mutations did not show a difference in overall survival (HR = 1.0, p-value = 0.57). To further validate the improved specificity of the PMC classifier TMB was used as a surrogate marker, using the PMC classifier 98% of tumors with LOP showed hypermutation (TMB > 20mut/Mb), vs. 53% called functional by polyphen-2. A retrospective analysis of MD Anderson CRC patients identified 25 patients with LOP POLE mutation, who had improved OS relative to 267 CRC patients with passenger POLE mutation (not-yet-reached vs. 70 mo, HR:4.2, p = 0.028). Four metastatic CRC patients with LOP POLE mutation were treated with immune therapy (nivolumab, or ipilimumab/nivolumab) in 2nd or 3rd line, all four achieved objective response and remain on therapy (mean time on treatment 15 mo). Conclusions: The PMC classifier specifically identifies mutations in POLE that cause loss of the proofreading function, outperforming both manually curated databases and machine learning-based methods. Clinical trials that use POLE mutation as a selection criteria for immune therapy should be restricted to just those POLE mutations that cause LOP.

Chromatin accessibility of primary human cancers ties regional mutational processes with tissues of origin

Regional mutagenesis in cancer genomes associates with DNA replication timing (RT) and chromatin accessibility (CA) of normal cells, however human cancer epigenomes remain uncharacterized in this context. Here we model megabase-scale mutation frequencies in 2517 cancer genomes with 773 CA and RT profiles of cancers and normal cells. We find that CA profiles of matching cancers, rather than normal cells, predict regional mutagenesis and mutational signatures, indicating that most passenger mutations follow the epigenetic landscapes of transformed cells. Carcinogen-induced and unannotated signatures show the strongest associations with epigenomes. Associations with normal cells in melanomas, lymphomas and SBS1 signatures suggest earlier occurrence of mutations in cancer evolution. Frequently mutated regions unexplained by CA and RT are enriched in cancer genes and developmental pathways, reflecting contributions of localized mutagenesis and positive selection. These results underline the complex interplay of mutational processes, genome function and evolution in cancer and tissues of origin.

Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

Sequence neighborhoods enable reliable prediction of pathogenic mutations in cancer genomes

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on utilizing the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5’ and 3’ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments gave comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with two other commonly used driver prediction tools (CONDEL and Mutation Taster) outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of utilizing raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

Genetic Analysis of Multiple Myeloma Identifies Cytogenetic Alterations Implicated in Disease Complexity and Progression

Multiple myeloma (MM) is a genetically heterogeneous disease characterized by genomic chaos making it difficult to distinguish driver from passenger mutations. In this study, we integrated data from whole genome gene expression profiling (GEP) microarrays and CytoScan HD high-resolution genomic arrays to integrate GEP with copy number variations (CNV) to more precisely define molecular alterations in MM important for disease initiation, progression and poor clinical outcome. We utilized gene expression arrays from 351 MM samples and CytoScan HD arrays from 97 MM samples to identify eight CNV events that represent possible MM drivers. By integrating GEP and CNV data we divided the MM into eight unique subgroups and demonstrated that patients within one of the eight distinct subgroups exhibited common and unique protein network signatures that can be utilized to identify new therapeutic interventions based on pathway dysregulation. Data also point to the central role of 1q gains and the upregulated expression of ANP32E, DTL, IFI16, UBE2Q1, and UBE2T as potential drivers of MM aggressiveness. The data presented here utilized a novel approach to identify potential driver CNV events in MM, the creation of an improved definition of the molecular basis of MM and the identification of potential new points of therapeutic intervention.

A deep learning system can classify primary and metastatic cancers using passenger mutation patterns, but how does it really compare to current pathological diagnosis in a well-designed diagnostic accuracy study?

In a recent study published in Nature Communications by Jiao W et al, a deep learning classifier was trained to predict cancer type based on somatic passenger mutations identified using whole genome sequencing (WGS) as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. The data show patterns of somatic passenger mutations differ between tumours with different cell of origin. Overall, the system had an accuracy of 91% in a cross-validation setting using the training set, and 88% and 83% using external validation sets of primary and metastatic tumours respectively. Surprisingly, this is claimed to be twice as accurate as trained pathologists, based on a 27 year old reference from 1993 prior to availability and routine utilisation of immunohistochemistry (IHC) in diagnostic pathology and is not a reflection of current diagnostic standards. We discuss the vital role of pathology in patient care and the importance of using international standards if deep learning methods are to be used in the clinical setting.