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We present a unique database of tumor samples from 399 multiple myeloma (MM) patients, whose functional genomic landscape was assessed by integrating ex vivo drug sensitivity to 138 drugs, clinical variables, cytogenetics, mutational profiles and transcriptomes. These analyses revealed a novel MM transcriptomic topology that, when associated with ex vivo drug sensitivity, generates “footprints” having both mechanistic and predictive applications. We validated the transcriptomic footprint for the nuclear export inhibitor selinexor in two clinical trials, BOSTON and MCC17814, and showed such a gene signature can accurately classify clinical response and suggest mechanism of resistance. Finally, we propose a novel evolutionary-based therapeutic strategy involving sequential therapy of monoclonal antibody daratumumab and selinexor, based on anti-correlative transcriptomic footprints, which was validated in two clinical trials (STOMP and XPORT-MM-028). Collectively, this database and computational framework can be leveraged to inform underlying biology, and to identify novel therapeutic strategies to improve treatment of MM.

Early breast carcinogenesis is characterized by an intermittently hypoxic, acidic and nutrient-deprived microenvironment, eventually selecting for the invasive, aerobically glycolytic phenotype known as Warburg Effect (WE), a universal cancer hallmark. We have reproduced the emergence of WE in vitro, by subjecting the nontumorigenic breast epithelial cell line MCF10A to multiple intermittent cycles of media starvation followed by recovery in growth media. Clonal expansion of surviving cells demonstrated increased lactate production in aerobic conditions (WE). A comprehensive molecular characterization, through WES and single cell multiomics, was performed to study the relationship between mutations, CNA, cytogenetic abnormalities, transcriptional dysregulation, and epigenetic modifications. Mutational analysis of 6 WE clones identified 249 shared mutations, including HRAS(G12V), BRAF(P523L) and ALK(Q180X). COSMIC mutational signature 18 (ROS-induced) was identified as the main DNA lesion across all WE clones, suggesting oxidative stress as a main source of mutagenesis associated with WE development. Non-synonymous mutations in FUS, HDAC3, EZH2, MAZ, KMTD2, MET, and other oncogenes were found in the clones, consisting in secondary events in the WE transformation. Geneset enrichment analysis identified over-expression of MYC/E2F targets, MTORC1 signaling, glycolysis and cell cycle pathways, as well as under-expression of EMT/cell adhesion pathways, in WE clones. CNA, based on a consensus from WES, scRNAseq (inferCNV) and scATACseq (epiAneufinder), confirmed amplification of MYC, E2F1 and MYBL2, as well as Gain(19q) and Gain(20) in all WE clones, as well as loss of TP63 in 5 out of 6 WE clones. Integrative single cell transcriptome/chromatin accessibility analysis (ArchR) identified decreased accessibility and expression of EMT-related genes, as a consequence of differential expression of pioneer transcription factors (i.e. reduced expression of TP63, due to deletion, led to silencing of TP53/63/73 targeted genes), as well as well-established EMT regulators (ZEB1, SNAI1/2, NFKB1), among others. We propose that the WE phenotype emerges after intermittent nutrient-derived oxidative stress induces increased expression of HRAS-regulated genes, acutely increasing mutation rate and transcriptionally inducing an EMT-like state. Once HRAS(G12V), MYC/E2F1 amplification and TP53 deletions are fixed in the population, the clones will maintain the ROS production and EMT-like state, even in optimum growth media conditions, completing the transition from an acute response to a chronic-inheritable phenotype known as WE. These findings reinforce the importance of microenvironmental stress driving early carcinogenesis, bridges the gap between environment and genomic-driven causes of malignant transformation and provides testable hypotheses of causal events in connection with WE. Citation Format: Rafael Renatino Canevarolo, Praneeth Reddy Sudalagunta, Joon-Hyun Song, Erez Persi, Mehdi Damaghi, Ariosto Siqueira Silva. Multiomic landscape of evolution of Warburg effect identifies drivers of breast cancer. [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 4784.

Small-scale regulatory networks can model known biological processes; while large-scale genome-wide datasets can identify novel mechanisms. We developed a biophysical modeling framework that combines the accuracy of small-scale networks with the power of large-scale datasets. As a proof of principle, we implemented this framework on a cohort of 844 multiple myeloma (MM) patients’ (and 1092 TCGA breast cancer patients) z-normalized RNAseq data using t-Distributed Stochastic Neighbor Embedding to construct a disease-specific transcriptomic map, where genes closer to each other co-express within the cohort. Fuzzy c-means clustering is carried out to identify clusters of genes that are likely regulated by a common transcription factor (TF). We construct a gene regulatory network (GRN) for each cluster of co-expressing genes on a disease-specific transcriptomic map by identifying upstream TFs for each cluster using publicly available databases ENCODE and ChEA, and kinases that phosphorylate these TFs using PhosphoSitePlus and PhosphoPoint. An exhaustive list of TFs and kinases are reduced to a few key predictor variables using regression tree modeling for each gene in that cluster. This leads to a cascading network of kinases that phosphorylate TFs, which regulate expression of genes in a cluster. We derived a mechanistic model from first-principles to define functional relationships governing the GRN; where transcription, translation, and post-translational modifications are modeled using first-order reversible reaction kinetic equations. The patient-specific rate constants of the model are parametrized by single sample gene set enrichment analysis scores of key KEGG pathways like ribosome, protein synthesis, RNA degradation, etc. The system of differential equations, under steady-state, reduce to an algebraic equation that can predict the expression of every gene in a cluster from the expression of its upstream TFs and kinases alone, which is fitted to RNAseq data of 422 MM patients to estimate undetermined parameters. The remaining patients’ data is used to estimate the accuracy of the model using Pearson’s correlation (model predicted vs actual) coefficient, r. Out of 16,738 genes, 7,936 were predicted accurately (r>0.5), while the remaining genes were shown to have a significant overlap (hypergeometric test; p-value Citation Format: Praneeth Reddy Sudalagunta, Rafael Renatino Canevarolo, Mark Meads, Maria Coelho Silva, Xiaohong Zhao, Raghunandan Reddy Alugubelli, Joon-hyun Song, Erez Persi, Mehdi Damaghi, Kenneth H. Shain, Ariosto Silva. A novel gene regulatory network model identifies master regulators in cancer. [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 4313.

The clinical utility of histone/protein deacetylase (HDAC) inhibitors in combinatorial regimens with proteasome inhibitors for patients with relapsed and refractory multiple myeloma (MM) is often limited by excessive toxicity due to HDAC inhibitor promiscuity with multiple HDACs. Therefore, more selective inhibition minimizing off-target toxicity may increase the clinical effectiveness of HDAC inhibitors. We demonstrated that plasma cell development and survival are dependent upon HDAC11, suggesting this enzyme is a promising therapeutic target in MM. Mice lacking HDAC11 exhibited markedly decreased plasma cell numbers. Accordingly, in vitro plasma cell differentiation was arrested in B cells lacking functional HDAC11. Mechanistically, we showed that HDAC11 is involved in the deacetylation of IRF4 at lysine103. Further, targeting HDAC11 led to IRF4 hyperacetylation, resulting in impaired IRF4 nuclear localization and target promoter binding. Importantly, transient HDAC11 knockdown or treatment with elevenostat, an HDAC11-selective inhibitor, induced cell death in MM cell lines. Elevenostat produced similar anti-MM activity in vivo, improving survival among mice inoculated with 5TGM1 MM cells. Elevenostat demonstrated nanomolar ex vivo activity in 34 MM patient specimens and synergistic activity when combined with bortezomib. Collectively, our data indicated that HDAC11 regulates an essential pathway in plasma cell biology establishing its potential as an emerging theraputic vulnerability in MM.