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.

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.

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.

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.

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.

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.

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.

PLK1 stabilizes a MYC-dependent kinase network in aggressive B cell lymphomas.

Concordant activation of MYC and BCL-2 oncoproteins in double-hit lymphoma (DHL) results in aggressive disease that is refractory to treatment. By integrating activity-based proteomic profiling and drug screens, polo-like kinase-1 (PLK1) was identified as an essential regulator of the MYC-dependent kinome in DHL. Notably, PLK1 was expressed at high levels in DHL, correlated with MYC expression, and connoted poor outcome. Further, PLK1 signaling augmented MYC protein stability, and in turn, MYC directly induced PLK1 transcription, establishing a feed-forward MYC-PLK1 circuit in DHL. Finally, inhibition of PLK1 triggered degradation of MYC and of the antiapoptotic protein MCL-1, and PLK1 inhibitors showed synergy with BCL-2 antagonists in blocking DHL cell growth, survival, and tumorigenicity, supporting clinical targeting of PLK1 in DHL.

Patient Enrichment for Precision-Based Cancer Clinical Trials: Using Prospective Cohort Surveillance as an Approach to Improve Clinical Trials.

Technological advances have led to the identification of biomarkers and development of novel target-based therapies. While some novel therapies have improved patient outcomes, the prevalence and diversity of biomarkers and targets in patient populations, especially patients with cancer, has created a challenge for the design and performance of clinical trials. To address this challenge we propose that prospective cohort surveillance of patients may be a solution to promote clinical trial matching for patients in need.

Unification of de novo and acquired ibrutinib resistance in mantle cell lymphoma.

The novel Bruton's tyrosine kinase inhibitor ibrutinib has demonstrated high response rates in B-cell lymphomas; however, a growing number of ibrutinib-treated patients relapse with resistance and fulminant progression. Using chemical proteomics and an organotypic cell-based drug screening assay, we determine the functional role of the tumour microenvironment (TME) in ibrutinib activity and acquired ibrutinib resistance. We demonstrate that MCL cells develop ibrutinib resistance through evolutionary processes driven by dynamic feedback between MCL cells and TME, leading to kinome adaptive reprogramming, bypassing the effect of ibrutinib and reciprocal activation of PI3K-AKT-mTOR and integrin-ß1 signalling. Combinatorial disruption of B-cell receptor signalling and PI3K-AKT-mTOR axis leads to release of MCL cells from TME, reversal of drug resistance and enhanced anti-MCL activity in MCL patient samples and patient-derived xenograft models. This study unifies TME-mediated de novo and acquired drug resistance mechanisms and provides a novel combination therapeutic strategy against MCL and other B-cell malignancies.

Use of the Total Cancer Care System to Enrich Screening for CD30-Positive Solid Tumors for Patient Enrollment Into a Brentuximab Vedotin Clinical Trial: A Pilot Study to Evaluate Feasibility.

BACKGROUND: One approach to identify patients who meet specific eligibility criteria for target-based clinical trials is to use patient and tumor registries to prescreen patient populations. OBJECTIVE: Here we demonstrate that the Total Cancer Care (TCC) Protocol, an ongoing, observational study, may provide a solution for rapidly identifying patients with CD30-positive tumors eligible for CD30-targeted therapies such as brentuximab vedotin. METHODS: The TCC patient gene expression profiling database was retrospectively screened for CD30 gene expression determined using HuRSTA-2a520709 Affymetrix arrays (GPL15048). Banked tumor tissue samples were used to determine CD30 protein expression by semiquantitative immunohistochemistry. Statistical comparisons of Z- and H-scores were performed using R statistical software (The R Foundation), and the predictive value, accuracy, sensitivity, and specificity of CD30 gene expression versus protein expression was estimated. RESULTS: As of March 2015, 120,887 patients have consented to the institutional review board-approved TCC Protocol. A total of 39,157 fresh frozen tumor specimens have been collected, from which over 14,000 samples have gene expression data available. CD30 RNA was expressed in a number of solid tumors; the highest median CD30 RNA expression was observed in primary tumors from lymph node, soft tissue (many sarcomas), lung, skin, and esophagus (median Z-scores 1.011, 0.399, 0.202, 0.152, and 1.011, respectively). High level CD30 gene expression significantly enriches for CD30-positive protein expression in breast, lung, skin, and ovarian cancer; accuracy ranged from 72% to 79%, sensitivity from 75% to 100%, specificity from 70% to 76%, positive predictive value from 20% to 40%, and negative predictive value from 95% to 100%. CONCLUSIONS: The TCC gene expression profiling database guided tissue selection that enriched for CD30 protein expression in a number of solid tumor types. Such an approach may improve screening efficiency for enrolling patients into biomarker-based clinical trials.

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.

Treatment of acquired drug resistance in multiple myeloma by combination therapy with XPO1 and topoisomerase II inhibitors.

BACKGROUND: Acquired drug resistance is the greatest obstacle to the successful treatment of multiple myeloma (MM). Despite recent advanced treatment options such as liposomal formulations, proteasome inhibitors, immunomodulatory drugs, myeloma-targeted antibodies, and histone deacetylase inhibitors, MM is still considered an incurable disease. METHODS: We investigated whether the clinical exportin 1 (XPO1) inhibitor selinexor (KPT-330), when combined with pegylated liposomal doxorubicin (PLD) or doxorubicin hydrochloride, could overcome acquired drug resistance in multidrug-resistant human MM xenograft tumors, four different multidrug-resistant MM cell lines, or ex vivo MM biopsies from relapsed/refractory patients. Mechanistic studies were performed to assess co-localization of topoisomerase II alpha (TOP2A), DNA damage, and siRNA knockdown of drug targets. RESULTS: Selinexor was found to restore sensitivity of multidrug-resistant 8226B25, 8226Dox6, 8226Dox40, and U266PSR human MM cells to doxorubicin to levels found in parental myeloma cell lines. NOD/SCID-γ mice challenged with drug-resistant or parental U266 human MM and treated with selinexor/PLD had significantly decreased tumor growth and increased survival with minimal toxicity. Selinexor/doxorubicin treatment selectively induced apoptosis in CD138/light-chain-positive MM cells without affecting non-myeloma cells in ex vivo-treated bone marrow aspirates from newly diagnosed or relapsed/refractory MM patients. Selinexor inhibited XPO1-TOP2A protein complexes (proximity ligation assay), preventing nuclear export of TOP2A in both parental and multidrug-resistant MM cell lines. Selinexor/doxorubicin treatment significantly increased DNA damage (comet assay/γ-H2AX) in both parental and drug-resistant MM cells. TOP2A knockdown reversed both the anti-tumor effect and significantly reduced DNA damage induced by selinexor/doxorubicin treatment. CONCLUSIONS: The combination of an XPO1 inhibitor and liposomal doxorubicin was highly effective against acquired drug resistance in in vitro MM models, in in vivo xenograft studies, and in ex vivo samples obtained from patients with relapsed/refractory myeloma. This drug combination synergistically induced TOP2A-mediated DNA damage and subsequent apoptosis. In addition, based on our preclinical data, we have initiated a phase I/II study with the XPO1 inhibitor selinexor and PLD (ClinicalTrials.gov NCT02186834). Initial results from both preclinical and clinical trials have shown significant promise for this drug combination for the treatment of MM.

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.

Turning the tide against cancer through sustained medical innovation: the pathway to progress.

An ever-expanding understanding of the molecular basis of the more than 200 unique diseases collectively called cancer, combined with efforts to apply these insights to clinical care, is forming the foundation of an era of personalized medicine that promises to improve cancer treatment. At the same time, these extraordinary opportunities are occurring in an environment of intense pressure to contain rising healthcare costs. This environment presents a challenge to oncology research and clinical care, because both are becoming progressively more complex and expensive, and because the current tools to measure the cost and value of advances in care (e.g., comparative effectiveness research, cost-effectiveness analysis, and health technology assessments) are not optimized for an ecosystem moving toward personalized, patient-centered care. Reconciling this tension will be essential to maintaining progress in a cost-constrained environment, especially because emerging innovations in science (e.g., increasing identification of molecular biomarkers) and in clinical process (implementation of a learning healthcare system) hold potential to dramatically improve patient care, and may ultimately help address the burden of rising costs. For example, the rapid pace of innovation taking place within oncology calls for increased capability to integrate clinical research and care to enable continuous learning, so that lessons learned from each patient treated can inform clinical decision making for the next patient. Recognizing the need to define the policies required for sustained innovation in cancer research and care in an era of cost containment, the stakeholder community must engage in an ongoing dialogue and identify areas for collaboration. This article reflects and seeks to amplify the ongoing robust discussion and diverse perspectives brought to this issue by multiple stakeholders within the cancer community, and to consider how to frame the research and regulatory policies necessary to sustain progress against cancer in an environment of constrained resources.

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.

A microenvironment-mediated c-Myc/miR-548m/HDAC6 amplification loop in non-Hodgkin B cell lymphomas.

A dynamic interaction occurs between the lymphoma cell and its microenvironment, with each profoundly influencing the behavior of the other. Here, using a clonogenic coculture growth system and a xenograft mouse model, we demonstrated that adhesion of mantle cell lymphoma (MCL) and other non-Hodgkin lymphoma cells to lymphoma stromal cells confers drug resistance, clonogenicity, and induction of histone deacetylase 6 (HDAC6). Furthermore, stroma triggered a c-Myc/miR-548m feed-forward loop, linking sustained c-Myc activation, miR-548m downregulation, and subsequent HDAC6 upregulation and stroma-mediated cell survival and lymphoma progression in lymphoma cell lines, primary MCL and other B cell lymphoma cell lines. Treatment with an HDAC6-selective inhibitor alone or in synergy with a c-Myc inhibitor enhanced cell death, abolished cell adhesion­mediated drug resistance, and suppressed clonogenicity and lymphoma growth ex vivo and in vivo. Together, these data suggest that the lymphoma-stroma interaction in the lymphoma microenvironment directly impacts the biology of lymphoma through genetic and epigenetic regulation, with HDAC6 and c-Myc as potential therapeutic targets.