Abstract: The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on 2 other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later time points before therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.

Abstract: A fundamental question in biology, central to our understanding of cancer and other pathologies, is determining how different cell types coordinate to form and maintain tissues. Recognizing the distinct features and capabilities of the cells that compose these tissues is critical. Unfortunately, the complexity of tissues often hinders our ability to distinguish between neighboring cell types and, in turn, scrutinize their transcriptomes and generate reliable and tractable cell models for studying their inherently different biologies. A lack of comprehensive methods to identify, isolate, and culture each cell type from many tissues have impeded progress. Here, we will describe such a method for the breadth of cell types composing the human breast. Furthermore, we have sequenced mRNAs from each purified population and investigated transcriptional patterns that reveal their distinguishing features. These analyses have exposed differentially expressed genes and enriched biological pathways that capture the essence of each cell type, along with transcripts that display intriguing expression patterns. These data, analytic tools, and transcriptional analyses form a rich resource whose exploration provides remarkable insights into the inner workings of the cell types composing the breast, thus furthering our understanding of the rules governing normal cell and tissue function. Citation Format: Katelyn Del Toro, Rosalyn W. Sayaman, Kate Thi, Yamhilette Licon-Munoz, William C. Hines. A cellular and transcriptomic dissection of the human breast for studying mechanisms of cell and tissue function. [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 4652.

Abstract: Abstract 3564 The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Whereas some patients develop aggressive disease requiring early treatment, others can have highly indolent disease and not require therapy for many years. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Microarray studies have highlighted differences in mRNA levels found between such CLL subgroups. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for disease progression. The clinical characterization of patients, blood-sample preparation, and microarray processing all follow the unified protocol implemented by the Microarray Innovations in LEukemia (MILE) program, which proposed standards for microarray-based assays in the diagnosis and sub-classification of leukemia. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection (Fig. 1A). The prognostic power of these subnetworks then was validated on a second cohort of patients in the MILE study and on another set of CLL patients evaluated outside the MILE program (Fig. 1B). The identified subnetworks could assess the risk for requiring therapy at the time of tissue collection more accurately than established markers (Fig. 1C). Statistical analyses of these and the microarray data collected in prior studies revealed the greatest divergence in gene expression was observed using samples collected within 1 year of diagnosis. Thereafter there was increasing congruence in the expression levels of some subnetworks between patients over time. Moreover, the expression levels of such predictive subnetworks could evolve in patients with otherwise indolent disease characteristics to resemble those associated with patients found to have aggressive disease at diagnosis. These analyses suggest that degenerate pathways apparently converge into common pathways that are associated with disease progression. We conclude that, in addition to having predictive power, these identified subnetworks represent an array of pathways associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.Figure 1Use of expression levels of genes versus subnetworks to stratify patient samples. (A) Five-fold cross validation on the 130 patients from UCSD. Survival analyses on SC→TX are shown for both the low (dashed lines) and high (solid lines) risk groups predicted by subnetwork signatures (red lines) or by gene signatures (green lines). (B-C) Survival curves on SC→TX for the 17 European patients (B) or for the patient cohort in Friedman et al (2009) (C). The two risk groups are predicted by two sets of markers developed on the UCSD cohort, including the 38 subnetworks (red lines) and the top 230 genes (green lines).Figure 1. Use of expression levels of genes versus subnetworks to stratify patient samples. (A) Five-fold cross validation on the 130 patients from UCSD. Survival analyses on SC→TX are shown for both the low (dashed lines) and high (solid lines) risk groups predicted by subnetwork signatures (red lines) or by gene signatures (green lines). (B-C) Survival curves on SC→TX for the 17 European patients (B) or for the patient cohort in Friedman et al (2009) (C). The two risk groups are predicted by two sets of markers developed on the UCSD cohort, including the 38 subnetworks (red lines) and the top 230 genes (green lines). Disclosures: Foa: Roche: Consultancy, Speakers Bureau.

Abstract: An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here, we develop a resource of synthetic-lethal interactions between genes mutated in cancer, including many tumor suppressor genes (TSG), and selective chemical inhibitors including many FDA-approved drugs, using an integrative multi-species approach. Whereas, targeting oncogenes with either chemical inhibitors or therapeutic antibodies has proven to be highly effective for cancer therapy, it is not currently feasible to restore the function of mutated or deleted TSGs in the clinical setting. Rather than targeting a TSG directly, it is possible to exploit a “synthetic lethal” genetic interactions between the TSG and another gene, such that simultaneous disruption of both gene functions causes rapid and selective cell death. For example, cells deficient for BRCA1 have a reduced capacity for repairing double-stranded DNA breaks and are especially vulnerable to further perturbations in alternate DNA repair pathways. Oliparib, an FDA-approved drug, exploits this principle by targeting a component of the base excision repair pathway, PARP1, thus causing selective cell death in BRCA1-/- or BRCA2 -/- cells. Recent efforts to map synthetic-lethal interactions in cancer typically fall into one of several categories. First, populations of tumor genomes may be analyzed statistically to detect pairs of genes that are seldom co-mutated in the same tumor, with one interpretation being that loss-of-function of both genes is synthetically lethal. While promising, such approaches are under-powered to test many relevant interactions, due to the already low frequency of mutation for most TSGs and the quadratic number of gene pairs that must be tested for co-mutation. Second, synthetic-lethal interactions may be mapped by directed combinatorial disruptions in human cell lines, using pairwise RNAi knockdowns, RNAi or drug treatments in cell lines with TSG loss-of-function or, conceivably in the near future, the CRISPR-Cas9 system. While such directed approaches can test relevant interactions in an unbiased manner, the largest screens performed to-date (∼10,000 gene pairs) still fall quite short of the required throughput to interrogate the potential interaction space of millions of human gene pairs involving a TSG. A complementary strategy for mapping synthetic lethal interactions in cancer is to leverage conservation with genetic interactions first identified in model species. In the yeasts S. cerevisiae and S. pombe, techniques such as synthetic genetic arrays (SGA) and Pombe Epistasis Mapper (PEM) enable genetic interactions to be measured in an unbiased and high-throughput manner, with minimal off-target effects since the genes are disrupted by complete and specific knockout of the open reading frame. Such interactions are numerous and found to be significantly conserved across species, especially for the core conserved pathways in which TSGs typically operate such as the cell cycle, genome maintenance and metabolic growth. Many TSGs important for human cancer were first identified and studied in yeast, which also provides an accessible model system in which to study mechanism of action for effects first observed in humans. Nonetheless, it is unclear whether and to what extent synthetic lethal interactions observed for core conserved processes can be ultimately translated for clinical application. Multiple factors have been postulated to influence whether an interaction will be translatable, including genetic, epigenetic, and environmental context as well as the strength, redundancy, and network topology of the interaction. To study such factors, however, would require a large cross-species dataset of genetic interactions relevant to cancer genes and functions. Here, we generate a comprehensive multi-species synthetic lethal network as a resource for the study of cancer and the design of targeted therapy. Leveraging the throughput and precise gene disruption of SGA technology, we experimentally test ∼78,000 potential interactions to generate a network that includes quantitative tests for interaction among all yeast orthologs of human TSGs and genes that are currently targetable by selective inhibitors (“druggable” targets or DT). Guided by these data, we target 2,352 TSG-drug combinations in human HeLa cells, resulting in a validated network of 172 “deeply conserved” interactions, called CoCaNet (Conserved Cancer Network). Having created this resource of conserved synthetic lethal interactions we explored three possible applications. First we demonstrate that synthetic lethal relationships in the conserved network are strongly predictive of cell survival in orthogonal survival assays and in alternate cell lines. We validated synthetic lethal interactions between the TSG RAD17 and all five synthetic lethal partners in CoCaNet (CHEK1, CHEK2, TOP2, TOP3A, CSNK1G1) in clonogenic assays using HeLa cells. For the TSG XRCC3 five of seven synthetic lethal partners (HDAC1, HDAC2, HDAC6, IMPDH1, RABGGTB) were confirmed in clonogenic assay in LN428 glioblastoma cells. Second, we examined the clinical relevance of these synthetic lethal networks. Using gene expression and clinical survival data of breast cancer patients from the METABRIC database (Curtis et al., Nature 2012) we tested our hypothesis that co-under expression of genes in CoCaNet would reduce the fitness of a tumor and associate with better clinical outcome. As expected overall survival was 8.6 years for those patients in the top 90th percentile of synthetic lethal interactions vs. 7.3 years for the 10th percentile (log-rank p & lt; 0.0005). We also assessed the extent to which CoCaNet might serve as a source of potentially relevant interactions for a broad population of cancer patients. CoCaNet includes a total of 59 unique TSGs and provides an average of ∼3 conserved interactions for each. Based on analysis of 7,394 cases profiled by TCGA across 22 tumor types, we found that at least one of these TSGs is either mutated or homozygously deleted in approximately 42% of patients, with 19% of patients having alterations to two or more of these TSGs. One specific example of how this network could be used for precision medicine is seen with ATM. TOP3A, commonly targeted by irinotecan in metastatic colorectal cancer (mCRC), in which ATM is mutated in 18% of tumors, was found to be a synthetic-sick/lethal interaction partner with ATM. These synthetic lethal data suggest that FOLFIRI (5-flourouracil plus irinotecan) may be the preferred initial regimen in ATM mutant mCRC. Although these finding would clearly need to be validated prior to influencing a clinical decision, the case of ATM-TOP3A is just one example of how the CoCaNet could be used to derive potentially clinically actionable information from a tumor genome. Third, we use the overlapping yeast and human synthetic lethal networks to learn the ‘rules’ that govern whether an interaction observed in yeast will be conserved in humans. We annotated each gene pair with multiple observations including whether we had observed interaction conservation with yeast, the degree to which the genes are co-expressed, whether the gene products are linked by a protein-protein interaction, and whether the genes are known to co-function in the same Gene Ontology biological process. Training on the overlapping yeast and human networks we integrated these multiple lines of evidence into a combined Log Likelihood Score classifier. Applying this classifier of cross-species conservation to the complete yeast network we are able to predict an expanded human network of over 11,000 prioritized synthetic sick or lethal interactions for pre-clinical and ultimately clinical exploration, each backed by data from budding yeast for investigating drug mode of action. Citation Format: John Paul Shen, Rohith Srivas, Chih Cheng Yang, Su Ming Sun, Jian Feng Li, Andrew Gross, James Jensen, Kate Licon, Ana Bojoquez-Gomez, Kristin Klepper, Haico van Attikum, Pedro Aza-Blanc, Robert Sobol, Trey Ideker. A network of deeply conserved synthetic-lethal interactions for exploration of precision cancer therapy. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr NG02.

Abstract: Recent tumor genome analyses have cataloged hundreds of mutated genes, including a large population of rarely mutated genes with ill-defined roles in cancer. Here we find that, when considering the 10 genes of the iron-sulfur (Fe-S) pathway as an integrated whole, somatic mutations occur with a higher than expected frequency in 21.3% of bladder tumors. Mutations in these genes are associated with increased survival and mutational burden. Analysis of tumor gene expression highlights a relationship between these Fe-S mutations and signalling regulating proliferation and quiescence, with mutations promoting proliferative programs and abolishing maintenance of quiescence. In bladder tumor cells, we show that replication of this proliferative program, using an DYRK1A kinase inhibitor, promotes sensitivity to genotoxic agents. Furthermore, disruption of Fe-S genes ERCC2, MMS19, CIOA1, BRIP1 or POLD1 promotes cell proliferation and concomitant sensitivity to genotoxic agents. These results suggest a model in which Fe-S pathway mutations are selected due to their ability to bypass DNA damage checkpoints and drive proliferation, an aspect which simultaneously creates vulnerability to chemotherapy and resultant improvements in survival. Citation Format: Devin Patel, Fan Zheng, Michael Chen, Jason Kreisberg, Erica Silva, Trey Ideker, Kate Licon. Mutations in iron-sulfur cluster proteins are associated with decreased bladder cancer cell senescence and improved survival outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 217.