A genome-based model for adjusting radiotherapy dose (GARD): a retrospective, cohort-based study.

BACKGROUND: Despite its common use in cancer treatment, radiotherapy has not yet entered the era of precision medicine, and there have been no approaches to adjust dose based on biological differences between or within tumours. We aimed to assess whether a patient-specific molecular signature of radiation sensitivity could be used to identify the optimum radiotherapy dose. METHODS: We used the gene-expression-based radiation-sensitivity index and the linear quadratic model to derive the genomic-adjusted radiation dose (GARD). A high GARD value predicts for high therapeutic effect for radiotherapy; which we postulate would relate to clinical outcome. Using data from the prospective, observational Total Cancer Care (TCC) protocol, we calculated GARD for primary tumours from 20 disease sites treated using standard radiotherapy doses for each disease type. We also used multivariable Cox modelling to assess whether GARD was independently associated with clinical outcome in five clinical cohorts: Erasmus Breast Cancer Cohort (n=263); Karolinska Breast Cancer Cohort (n=77); Moffitt Lung Cancer Cohort (n=60); Moffitt Pancreas Cancer Cohort (n=40); and The Cancer Genome Atlas Glioblastoma Patient Cohort (n=98). FINDINGS: We calculated GARD for 8271 tissue samples from the TCC cohort. There was a wide range of GARD values (range 1·66-172·4) across the TCC cohort despite assignment of uniform radiotherapy doses within disease types. Median GARD values were lowest for gliomas and sarcomas and highest for cervical cancer and oropharyngeal head and neck cancer. There was a wide range of GARD values within tumour type groups. GARD independently predicted clinical outcome in breast cancer, lung cancer, glioblastoma, and pancreatic cancer. In the Erasmus Breast Cancer Cohort, 5-year distant-metastasis-free survival was longer in patients with high GARD values than in those with low GARD values (hazard ratio 2·11, 95% 1·13-3·94, p=0·018). INTERPRETATION: A GARD-based clinical model could allow the individualisation of radiotherapy dose to tumour radiosensitivity and could provide a framework to design genomically-guided clinical trials in radiation oncology. FUNDING: None.

Phase II study of pegylated liposomal doxorubicin, low-dose dexamethasone, and lenalidomide in patients with newly diagnosed multiple myeloma.

Our previous phase I/II trial of pegylated liposomal doxorubicin (PLD), low-dose dexamethasone, and lenalidomide in patients with relapsed and refractory myeloma showed an overall response rate of 75%, with 29% achieving ≥ VGPR. Here, we investigated this combination (PLD 30 or 40 mg/m(2) intravenously, day 1; dexamethasone 40 mg orally, days 1-4; lenalidomide 25 mg orally, days 1-21; administered every 28 days) in a phase II study in patients with newly diagnosed symptomatic multiple myeloma to determine its efficacy and tolerability (ClinicalTrials.gov NCT00617591). At best response, patients could proceed with high-dose melphalan or with maintenance lenalidomide and dexamethasone. In 57 patients, we found that the overall response rate and rate of very good partial response and better on intent-to-treat, our primary endpoints, were 77.2% and 42.1%, respectively, with responses per the International Myeloma Working Group. Median progression-free survival was 28 months (95% CI 18.1-34.8), with 1- and 2-year overall survival rates of 98.1 and 79.6%. During induction, grade 3/4 toxicities were neutropenia (49.1%), anemia (15.8%), thrombocytopenia (7%), fatigue (14%), febrile neutropenia (8.8%), and venous thromboembolic events (8.8%). During maintenance, grade 3/4 toxicities were mainly hematologic. We found this combination to be active in patients with newly diagnosed myeloma, with results comparable to other lenalidomide-based induction strategies without proteasome inhibition. In addition, maintenance therapy with lenalidomide was well tolerated.

Collaborative biomedicine in the age of big data: the case of cancer.

Biomedicine is undergoing a revolution driven by high throughput and connective computing that is transforming medical research and practice. Using oncology as an example, the speed and capacity of genomic sequencing technologies is advancing the utility of individual genetic profiles for anticipating risk and targeting therapeutics. The goal is to enable an era of "P4" medicine that will become increasingly more predictive, personalized, preemptive, and participative over time. This vision hinges on leveraging potentially innovative and disruptive technologies in medicine to accelerate discovery and to reorient clinical practice for patient-centered care. Based on a panel discussion at the Medicine 2.0 conference in Boston with representatives from the National Cancer Institute, Moffitt Cancer Center, and Stanford University School of Medicine, this paper explores how emerging sociotechnical frameworks, informatics platforms, and health-related policy can be used to encourage data liquidity and innovation. This builds on the Institute of Medicine's vision for a "rapid learning health care system" to enable an open source, population-based approach to cancer prevention and control.

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.

Background: Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME. Methods: We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset. Results: Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes. Conclusion: Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

Monitoring a nuclear factor-κB signature of drug resistance in multiple myeloma.

The emergence of acquired drug resistance results from multiple compensatory mechanisms acting to prevent cell death. Simultaneous monitoring of proteins involved in drug resistance is a major challenge for both elucidation of the underlying biology and development of candidate biomarkers for assessment of personalized cancer therapy. Here, we have utilized an integrated analytical platform based on SDS-PAGE protein fractionation prior to liquid chromatography coupled to multiple reaction monitoring mass spectrometry, a versatile and powerful tool for targeted quantification of proteins in complex matrices, to evaluate a well-characterized model system of melphalan resistance in multiple myeloma (MM). Quantitative assays were developed to measure protein expression related to signaling events and biological processes relevant to melphalan resistance in multiple myeloma, specifically: nuclear factor-κB subunits, members of the Bcl-2 family of apoptosis-regulating proteins, and Fanconi Anemia DNA repair components. SDS-PAGE protein fractionation prior to liquid chromatography coupled to multiple reaction monitoring methods were developed for quantification of these selected target proteins in amounts of material compatible with direct translation to clinical specimens (i.e. less than 50,000 cells). As proof of principle, both relative and absolute quantification were performed on cell line models of MM to compare protein expression before and after drug treatment in naïve cells and in drug resistant cells; these liquid chromatography-multiple reaction monitoring results are compared with existing literature and Western blots. The initial stage of a systems biology platform for examining drug resistance in MM has been implemented in cell line models and has been translated to MM cells isolated from a patient. The ultimate application of this platform could assist in clinical decision-making for individualized patient treatment. Although these specific assays have been developed to monitor MM, these techniques are expected to have broad applicability in cancer and other types of disease.

The T Cell Immunoscore as a Reference for Biomarker Development Utilizing Real-World Data from Patients with Advanced Malignancies Treated with Immune Checkpoint Inhibitors.

BACKGROUND: We aimed to determine the prognostic value of an immunoscore reflecting CD3+ and CD8+ T cell density estimated from real-world transcriptomic data of a patient cohort with advanced malignancies treated with immune checkpoint inhibitors (ICIs) in an effort to validate a reference for future machine learning-based biomarker development. METHODS: Transcriptomic data was collected under the Total Cancer Care Protocol (NCT03977402) Avatar® project. The real-world immunoscore for each patient was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells utilizing CIBERSORTx and the LM22 gene signature matrix. Then, the immunoscore association with overall survival (OS) was estimated using Cox regression and analyzed using Kaplan-Meier curves. The OS predictions were assessed using Harrell's concordance index (C-index). The Youden index was used to identify the optimal cut-off point. Statistical significance was assessed using the log-rank test. RESULTS: Our study encompassed 522 patients with four cancer types. The median duration to death was 10.5 months for the 275 participants who encountered an event. For the entire cohort, the results demonstrated that transcriptomics-based immunoscore could significantly predict patients at risk of death (p-value < 0.001). Notably, patients with an intermediate-high immunoscore achieved better OS than those with a low immunoscore. In subgroup analysis, the prediction of OS was significant for melanoma and head and neck cancer patients but did not reach significance in the non-small cell lung cancer or renal cell carcinoma cohorts. CONCLUSIONS: Calculating CD3+ and CD8+ T cell immunoscore using real-world transcriptomic data represents a promising signature for estimating OS with ICIs and can be used as a reference for future machine learning-based biomarker development.

Follicular dendritic cell-dependent drug resistance of non-Hodgkin lymphoma involves cell adhesion-mediated Bim down-regulation through induction of microRNA-181a.

Follicular dendritic cells (FDCs), an essential component of the lymph node microenvironment, regulate and support B-lymphocyte differentiation, survival, and lymphoma progression. Here, we demonstrate that adhesion of mantle cell lymphoma and other non-Hodgkin lymphoma cells to FDCs reduces cell apoptosis and is associated with decreased levels of the proapoptotic protein, Bim. Bim down-regulation is posttranscriptionally regulated via up-regulation of microRNA-181a (miR-181a). miR-181a overexpression decreases, whereas miR-181a inhibition increases Bim levels by directly targeting Bim. Furthermore, we found that cell adhesion-up-regulated miR-181a contributes to FDC-mediated cell survival through Bim down-regulation, implicating miR-181a as an upstream effector of the Bim-apoptosis signaling pathway. miR-181a inhibition and Bim upregulation significantly suppressed FDC-mediated protection against apoptosis in lymphoma cell lines and primary lymphoma cells. Thus, FDCs protect B-cell lymphoma cells against apoptosis, in part through activation of a miR-181a-dependent mechanism involving down-regulation of Bim expression. We demonstrate, for the first time, that cell-cell contact controls tumor cell survival and apoptosis via microRNA in mantle cell and other non-Hodgkin lymphomas. Regulation of microRNAs by B-cell-FDC interaction may support B-cell survival, representing a novel molecular mechanism for cell adhesion-mediated drug resistance and a potential therapeutic target in B-cell lymphomas.

microRNA expression profile and identification of miR-29 as a prognostic marker and pathogenetic factor by targeting CDK6 in mantle cell lymphoma.

Mantle cell lymphoma (MCL) is one of the most aggressive B-cell lymphomas. Although several protein-coding genes are altered, expression signature and importance of microRNA (miRNA) have not been well documented in this malignancy. Here, we performed miRNA expression profile in 30 patients with MCL using a platform containing 515 human miRNAs. Eighteen miRNAs were down-regulated and 21 were up-regulated in MCL compared with normal B lymphocytes. The most frequently altered miRNAs are decrease of miR-29a/b/c, miR-142-3p/5p, and miR-150 and increase of miR-124a and miR-155. Notably, expression levels of miR-29 family are associated with prognosis. The patients with significant down-regulated miR-29 had short survival compared with those who express relatively high levels of miR-29. The prognostic value of miR-29 is comparable with the Mantle Cell Lymphoma International Prognostic Index. Furthermore, we demonstrate miR-29 inhibition of CDK6 protein and mRNA levels by direct binding to 3'-untranslated region. Inverse correlation between miR-29 and CDK6 was observed in MCL. Because cyclin D1 overexpression is a primary event and exerts its function through activation of CDK4/CDK6, our results in primary MCL cells indicate that down-regulation of miR-29 could cooperate with cyclin D1 in MCL pathogenesis. Thus, our findings provide not only miRNA expression signature but also a novel prognostic marker and pathogenetic factor for this malignancy.

A novel TNF receptor-associated factor 6 binding domain mediates NF-kappa B signaling by the common cytokine receptor beta subunit.

GM-CSF, IL-3, and IL-5 are proinflammatory cytokines that control the production and function of myeloid and lymphoid cells. Their receptors are composed of a ligand-specific alpha subunit and a shared common signal-transducing beta subunit (beta common receptor or GM-CSFR beta [beta(c)]). The pleiotropic nature of biologic outcomes mediated by beta(c) and the presence of large, uncharacterized regions of its cytoplasmic domain suggest that much remains to be learned about its downstream signaling pathways. Although some previous work has attempted to link beta(c) with NF-kappaB activation, a definitive mechanism that mediates this pathway has not been described and, to date, it has not been clear whether the receptor can directly activate NF-kappaB. We demonstrate that NF-kappaB activation by beta(c) is dependent on TNFR-associated factor 6 (TRAF6) and that association of TRAF6 with beta(c) requires a consensus-binding motif found in other molecules known to interact with TRAF6. Furthermore, point mutation of this motif abrogated the ability of beta(c) to mediate NF-kappaB activation and reduced the viability of an IL-3-dependent hematopoietic cell line. Because this receptor plays a key role in hematopoiesis and the beta(c) cytoplasmic domain identified in this work mediates hematopoietic cell viability, this new pathway is likely to contribute to immune cell biology. This work is significant because it is the first description of a TRAF6-dependent signaling pathway associated with a type I cytokine receptor. It also suggests that TRAF6, a mediator of TNFR and TLR signaling, may be a common signaling intermediate in diverse cytokine receptor systems.

An ET-CURE pilot project supporting undergraduate training in cancer research, emerging technology, and health disparities.

The National Cancer Institute's Center to Reduce Cancer Health Disparities has created pilot training opportunities under the "Continuing Umbrella of Research Experiences" program that focus on emerging technologies. In this pilot project, an 18-month cancer biology research internship was reinforced with: instruction in an emerging technology (proteomics), a transition from the undergraduate laboratory to a research setting, education in cancer health disparities, and community outreach activities. A major goal was to provide underrepresented undergraduates with hands-on research experiences that are rarely encountered at the undergraduate level, including mentoring, research presentations, and participation in local and national meetings. These opportunities provided education and career development for the undergraduates, and they have given each student the opportunity to transition from learning to sharing their knowledge and from being mentored to mentoring others. Here, we present the concepts, curriculum, infrastructure, and challenges for this training program along with evaluations by both the students and their mentors.

Personalizing Radiotherapy Prescription Dose Using Genomic Markers of Radiosensitivity and Normal Tissue Toxicity in NSCLC.

INTRODUCTION: Cancer sequencing efforts have revealed that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy (RT), one of the most common cancer treatments, is prescribed on the basis of an empirical one-size-fits-all approach. We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation. METHODS: We have previously developed the genomic-adjusted radiation dose, a method that accounts for biological heterogeneity and can be used to predict optimal RT dose for an individual patient. In this article, we use genomic-adjusted radiation dose to characterize the biological imprecision of one-size-fits-all RT dosing schemes that result in both over- and under-dosing for most patients treated with RT. To elucidate this inefficiency, and therefore the opportunity for improvement using a personalized dosing scheme, we develop a patient-specific competing hazards style mathematical model combining the canonical equations for tumor control probability and normal tissue complication probability. This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics. RESULTS: Using data from two prospectively collected cohorts of patients with NSCLC, we validate the competing hazards model by revealing that it predicts the results of RTOG 0617. We report how the failure of RTOG 0617 can be explained by the biological imprecision of empirical uniform dose escalation which results in 80% of patients being overexposed to normal tissue toxicity without potential tumor control benefit. CONCLUSIONS: Our data reveal a tapestry of radiosensitivity heterogeneity, provide a biological framework that explains the failure of empirical RT dose escalation, and quantify the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.

Plasma cell dependence on histone/protein deacetylase 11 reveals a therapeutic target in multiple myeloma.

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.

Kinome siRNA screen identifies SMG-1 as a negative regulator of hypoxia-inducible factor-1alpha in hypoxia.

Hypoxia-inducible factor-1 (HIF-1) plays a central role in tumor progression by regulating genes involved in proliferation, glycolysis, angiogenesis, and metastasis. To improve our understanding of HIF-1 regulation by kinome, we screened a kinase-specific small interference RNA library using a hypoxia-response element (HRE) luciferase reporter assay under hypoxic conditions. This screen determined that depletion of cellular SMG-1 kinase most significantly modified cellular HIF-1 activity in hypoxia. SMG-1 is the newest and least studied member of the phosphoinositide 3-kinase-related kinase family, which consists of ATM, ATR, DNA-PKcs, mTOR, and SMG-1. We individually depleted members of the phosphoinositide 3-kinase-related kinase family, and only SMG-1 deficiency significantly augmented HIF-1 activity in hypoxia. We subsequently discovered that SMG-1 kinase activity was activated by hypoxia, and depletion of SMG-1 up-regulated MAPK activity under low oxygen. Suppressing cellular MAPK by silencing ERK1/2 or by treatment with U0126, a MAPK inhibitor, partially blocked the escalation of HIF-1 activity resulting from SMG-1 deficiency in hypoxic cells. Increased expression of SMG-1 but not kinase-dead SMG-1 effectively inhibited the activity of HIF-1alpha. In addition, cellular SMG-1 deficiency increased secretion of the HIF-1alpha-regulated angiogenic factor, vascular epidermal growth factor, and survival factor, carbonic anhydrase IX (CA9), as well as promoted the hypoxic cell motility. Taken together, we discovered that SMG-1 negatively regulated HIF-1alpha activity in hypoxia, in part through blocking MAPK activation.

Proteomic contributions to personalized cancer care.

Cancer impacts each patient and family differently. Our current understanding of the disease is primarily limited to clinical hallmarks of cancer, but many specific molecular mechanisms remain elusive. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies that improve patient prognosis are not widely available for most cancers. Individualized care plans, also described as personalized medicine, still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics holds great promise in contributing to the prevention and cure of cancer because it provides unique tools for discovery of biomarkers and therapeutic targets. As such, proteomics can help translate basic science discoveries into the clinical practice of personalized medicine. Here we describe how biological mass spectrometry and proteome analysis interact with other major patient care and research initiatives and present vignettes illustrating efforts in discovery of diagnostic biomarkers for ovarian cancer, development of treatment strategies in lung cancer, and monitoring prognosis and relapse in multiple myeloma patients.

Melphalan and Exportin 1 Inhibitors Exert Synergistic Antitumor Effects in Preclinical Models of Human Multiple Myeloma.

High-dose chemotherapy with melphalan followed by autologous transplantation is a first-line treatment for multiple myeloma. Here, we present preclinical evidence that this treatment may be significantly improved by the addition of exportin 1 inhibitors (XPO1i). The XPO1i selinexor, eltanexor, and KOS-2464 sensitized human multiple myeloma cells to melphalan. Human 8226 and U266 multiple myeloma cell lines and melphalan-resistant cell lines (8226-LR5 and U266-LR6) were highly sensitized to melphalan by XPO1i. Multiple myeloma cells from newly diagnosed and relapsed/refractory multiple myeloma patients were also sensitized by XPO1i to melphalan. In NOD/SCIDγ mice challenged with either parental 8226 or U266 multiple myeloma and melphalan-resistant multiple myeloma tumors, XPO1i/melphalan combination treatments demonstrated stronger synergistic antitumor effects than single-agent melphalan with minimal toxicity. Synergistic cell death resulted from increased XPO1i/melphalan-induced DNA damage in a dose-dependent manner and decreased DNA repair. In addition, repair of melphalan-induced DNA damage was inhibited by selinexor, which decreased melphalan-induced monoubiquitination of FANCD2 in multiple myeloma cells. Knockdown of FANCD2 was found to replicate the effect of selinexor when used with melphalan, increasing DNA damage (γH2AX) by inhibiting DNA repair. Thus, combination therapies that include selinexor or eltanexor with melphalan may have the potential to improve treatment outcomes of multiple myeloma in melphalan-resistant and newly diagnosed patients. The combination of selinexor and melphalan is currently being investigated in the context of high-dose chemotherapy and autologous transplant (NCT02780609). SIGNIFICANCE: Inhibition of exportin 1 with selinexor synergistically sensitizes human multiple myeloma to melphalan by inhibiting Fanconi anemia pathway-mediated DNA repair.

Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity.

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease.

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.

The bone marrow microenvironment as a tumor sanctuary and contributor to drug resistance.

The bone marrow microenvironment facilitates the survival, differentiation, and proliferation of hematopoietic cells. These cells are supported by fibroblast-like bone marrow stromal cells, osteoblasts, and osteoclasts which secrete soluble factors and extracellular matrix proteins that mediate these functions. This rich environment serves as a safe haven not only for normal and malignant hematopoietic cells, but also for epithelial tumor cells that metastasize to bone, offering protection from chemotherapeutic agents by common mechanisms. Soluble factors produced in the bone marrow, such as stromal cell-derived factor-1 and interleukin-6, mediate homing, survival, and proliferation of tumor cells, and integrin-mediated adhesion sequesters tumor cells to this protective niche. Environment-mediated drug resistance includes a combination of soluble factors and adhesion, and can be subdivided into soluble factor-mediated drug resistance and cell adhesion-mediated drug resistance. Because it is induced immediately by the microenvironment and is independent of epigenetic or genetic changes caused by the selective pressure of drug exposure, environment-mediated drug resistance is a form of de novo drug resistance. In this form of drug resistance, tumor cells are transiently and reversibly protected from apoptosis induced by both chemotherapy and physiologic mediators of cell death. This protection allows tumor cells to survive the insult of chemotherapy, leading to minimal residual disease, and thereby increases the probability for the development of acquired drug resistance.

Optimizing Cancer Treatment Using Game Theory: A Review.

Importance: While systemic therapy for disseminated cancer is often initially successful, malignant cells, using diverse adaptive strategies encoded in the human genome, almost invariably evolve resistance, leading to treatment failure. Thus, the Darwinian dynamics of resistance are formidable barriers to all forms of systemic cancer treatment but rarely integrated into clinical trial design or included within precision oncology initiatives. Observations: We investigate cancer treatment as a game theoretic contest between the physician's therapy and the cancer cells' resistance strategies. This game has 2 critical asymmetries: (1) Only the physician can play rationally. Cancer cells, like all evolving organisms, can only adapt to current conditions; they can neither anticipate nor evolve adaptations for treatments that the physician has not yet applied. (2) It has a distinctive leader-follower (or "Stackelberg") dynamics; the "leader" oncologist plays first and the "follower" cancer cells then respond and adapt to therapy. Current treatment protocols for metastatic cancer typically exploit neither asymmetry. By repeatedly administering the same drug(s) until disease progression, the physician "plays" a fixed strategy even as the opposing cancer cells continuously evolve successful adaptive responses. Furthermore, by changing treatment only when the tumor progresses, the physician cedes leadership to the cancer cells and treatment failure becomes nearly inevitable. Without fundamental changes in strategy, standard-of-care cancer therapy typically results in "Nash solutions" in which no unilateral change in treatment can favorably alter the outcome. Conclusions and Relevance: Physicians can exploit the advantages inherent in the asymmetries of the cancer treatment game, and likely improve outcomes, by adopting more dynamic treatment protocols that integrate eco-evolutionary dynamics and modulate therapy accordingly. Implementing this approach will require new metrics of tumor response that incorporate both ecological (ie, size) and evolutionary (ie, molecular mechanisms of resistance and relative size of resistant population) changes.

BCL2 Amplicon Loss and Transcriptional Remodeling Drives ABT-199 Resistance in B Cell Lymphoma Models.

Drug-tolerant "persister" tumor cells underlie emergence of drug-resistant clones and contribute to relapse and disease progression. Here we report that resistance to the BCL-2 targeting drug ABT-199 in models of mantle cell lymphoma and double-hit lymphoma evolves from outgrowth of persister clones displaying loss of 18q21 amplicons that harbor BCL2. Further, persister status is generated via adaptive super-enhancer remodeling that reprograms transcription and offers opportunities for overcoming ABT-199 resistance. Notably, pharmacoproteomic and pharmacogenomic screens revealed that persisters are vulnerable to inhibition of the transcriptional machinery and especially to inhibition of cyclin-dependent kinase 7 (CDK7), which is essential for the transcriptional reprogramming that drives and sustains ABT-199 resistance. Thus, transcription-targeting agents offer new approaches to disable drug resistance in B-cell lymphomas.