Andrew McPherson’s research while affiliated with Memorial Sloan Kettering Cancer Center and other places

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type–specific features. Using organ-matched healthy tissues, we identified the “nearest healthy” cell types in diverse cancers, demonstrating that the chromatin signature of basal-like–subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution in cancers with TP53 loss. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime, we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and the positive association between chromosomal instability and cGAS-STING pathway activation are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process in late stage ovarian cancer and link consequent genomic states with altered innate immune responses and immunosuppressive phenotypes.

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types. We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) further uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions. Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.

Clonal tree inference brings crucial insights to the analysis of tumor heterogeneity and cancer evolution. Recent progress in single cell sequencing has prompted a demand for more advanced probabilistic models of copy number evolution, coupled with inference methods which can account for the noisy nature of the data along with dependencies between adjacent sites in copy number profiles. We present VICTree, a Variational Inference based algorithm for joint Bayesian inference of clonal trees, together with a novel Tree-structured Mixture Hidden Markov Model (TSMHMM) which combines HMMs related through a tree with a mixture model. For the tree inference, we introduce a new algorithm, LARS, for sampling directed labeled multifurcating trees. To evaluate our proposed method, we conduct experiments on simulated data and on samples of multiple myeloma and breast cancer. We demonstrate VICTree's capacity for reliable clustering, clonal tree reconstruction, copy number evolution and the utility of the ELBO for model selection. Lastly, VICTree's results are compared in terms of quality and speed of inference to other state-of-the art methods. The code for VICTree is available on GitHub: github.com/Lagergren-Lab/victree.

Whole genome sequencing (WGS) enables the identification of all cancer associated biomarkers in a patient’s tumor genome. Whilst fresh frozen (FF) derived WGS data provides optimal data quality, the majority of clinical biospecimens are from formalin fixed paraffin embedded (FFPE) tissue which results in DNA damage and an increase in artifactual mutation calls. Development of analytical frameworks tailored to FFPE derived WGS data can unlock the potential of genome profiling in clinical oncology. We performed comprehensive WGS analysis on 58 matched FF/FFPE specimens derived from 3 cancer centers. Consensus calling detected high-confidence somatic mutations across variant classes including: single nucleotide variants (SNVs), insertions/deletions (indels), structural variants (SVs) and copy number aberrations (CNAs). For each sample, genome-wide mutational patterns including tumor mutational burden (TMB), SNV/indel signatures, and homologous recombination deficiency (HRD) scores were estimated. We developed a random forest based framework using 33 features to learn mutation patterns associated with FFPE artifacts and implemented a filtration strategy for FFPE derived WGS data within a clinical prototype analytical workflow. We identified a high degree of concordance (~91%, n=192/210) for oncogenic variants between FF/FFPE WGS data. Comparison of small mutation calls presented an average 2-fold increase in FFPE samples with a range up to 152x for SNVs and 43x for indels. However, this was not the case for SVs: -0.4x (range -0.8-1.4). We demonstrate that genome-wide mutation patterns were significantly affected, impacting estimates for TMB, HRD and signature contributions. On average, TMB was overestimated in FFPE (median=10.3, range: 1.4-94) versus FF (median=3.4, range:0.04-29.6). For 7 patients with evidence of HRD in FF, HRD scores did not reach statistical significance in FFPE. Mutational signatures in FFPE were enriched for COSMIC signatures 37 and 5. Our artifact classification model achieved ROC AUC of 97.5% and precision-recall of 98.9%. Post artifact filtration, precision in SNV/indel calling was increased from 49.3% to 93.4% and 61.8% to 82.7% respectively with no effect on driver alterations. This increased global signal concordance drastically, with comparable TMB scores (median 2.4; range .03-26.1) and improved cosine similarity for SNV/indel signatures (median 0.98; range 0.40-1). HRD was successfully detected in 7/7 patients from FFPE derived data post filtering with probability scores ranging from 0.76-1. We demonstrate that FFPE specimens harbor a variable increase in artifactual mutation burden in SNV/indels but not in SVs. We propose an effective filtering procedure which successfully removes FFPE related artifacts enabling accurate profiling of clinically relevant driver mutations and genome-wide mutation patterns from readily available FFPE-derived tumor specimens. Citation Format: Dylan Domenico, Gunes Gundem, Max F. Levine, Juanes E. Arango-Ossa, Pauline Robbe, Georgios Asimomitis, Cassidy Cobbs, Emily Stockfisch, Janine Senz, Dawn Cochrane, Neeman Mohibullah, Neerav Shukla, Sohrab P. Shah, Andrew McPherson, Anna Schuh, Andrew L. Kung, Elli Papaemmanuil. Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3142.

Patients treated with cytotoxic therapies, including autologous stem cell transplantation, are at risk for developing therapy-related myeloid neoplasms (tMN). Pre-leukemic clones (i.e., clonal hematopoiesis; CH) are detectable years before the development of these aggressive malignancies, though the genomic events leading to transformation and expansion are not well-defined. Here, leveraging distinctive chemotherapy-associated mutational signatures from whole-genome sequencing data and targeted sequencing of pre-chemotherapy samples, we reconstruct the evolutionary life-history of 39 therapy-related myeloid malignancies. A dichotomy is revealed, in which neoplasms with evidence of chemotherapy-induced mutagenesis from platinum and melphalan are hypermutated and enriched for complex structural variants (i.e., chromothripsis) while neoplasms with non-mutagenic chemotherapy exposures are genomically similar to de novo acute myeloid leukemia. Using chemotherapy-associated mutational signatures as temporal barcodes linked to a discrete clinical exposure in each patient's life, we estimate that several complex events and genomic drivers are acquired after chemotherapy is administered. For patients with prior multiple myeloma who were treated with high-dose melphalan and autologous stem cell transplantation, we demonstrate that tMN can develop from either a reinfused CH clone that escapes melphalan exposure and is selected following reinfusion, or from TP53-mutant CH that survives direct myeloablative conditioning and acquires melphalan-induced DNA-damage. Overall, we reveal a novel mode of tMN progression that is not reliant on direct mutagenesis or even exposure to chemotherapy. Conversely, for tMN that evolve under the influence of chemotherapy-induced mutagenesis, distinct chemotherapies not only select pre-existing CH, but also promote the acquisition of recurrent genomic drivers.

Chromosomal instability is a major challenge to patient stratification and targeted drug development for high-grade serous ovarian carcinoma (HGSOC). Here we show that somatic copy number alterations (SCNAs) in frequently amplified HGSOC cancer genes significantly correlate with gene expression and methylation status. We identify five prevalent clonal driver SCNAs (chromosomal amplifications encompassing MYC, PIK3CA, CCNE1, KRAS and TERT) from multi-regional HGSOC data and reason that their strong selection should prioritise them as key biomarkers for targeted therapies. We use primary HGSOC spheroid models to test interactions between in vitro targeted therapy and SCNAs. MYC chromosomal copy number is associated with in-vitro and clinical response to paclitaxel and in-vitro response to mTORC1/2 inhibition. Activation of the mTOR survival pathway in the context of MYC-amplified HGSOC is statistically associated with increased prevalence of SCNAs in genes from the PI3K pathway. Co-occurrence of amplifications in MYC and genes from the PI3K pathway is independently observed in squamous lung cancer and triple negative breast cancer. In this work, we show that identifying co-occurrence of clonal driver SCNA genes could be used to tailor therapeutics for precision medicine. Chromosomal instability is a major challenge to patient stratification and targeted drug development for high-grade serous ovarian carcinoma. Here we show that identification of clonal somatic copy number alterations in frequently amplified cancer genes could inform therapeutics for precision medicine.