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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.

Supplementary Data from HYD1-induced increase in reactive oxygen species leads to autophagy and necrotic cell death in multiple myeloma cells

HYD1 is a D–amino acid peptide that was previously shown to inhibit adhesion of prostate cancer cells to the extracellular matrix. In this study, we show that in addition to inhibiting adhesion of multiple myeloma (MM) cells to fibronectin, HYD1 induces cell death in MM cells as a single agent. HYD1-induced cell death was necrotic in nature as shown by: (a) decrease in mitochondrial membrane potential (Δψm), (b) loss of total cellular ATP, and (c) increase in reactive oxygen species (ROS) production. Moreover, HYD1 treatment does not result in apoptotic cell death because it did not trigger the activation of caspases or the release of apoptosis-inducing factor and endonuclease G from the mitochondria, nor did it induce double-stranded DNA breaks. HYD1 did initiate autophagy in cells; however, autophagy was found to be an adaptive response contributing to cell survival rather than the cause of cell death. We were further able to show that N-acetyl-L-cysteine, a thiol-containing free radical scavenger, partially protects MM cells from HYD1-induced death. Additionally, N-acetyl-L-cysteine blocked HYD1-induced as well as basal levels of autophagy, suggesting that ROS can potentially trigger both cell death and cell survival pathways. Taken together, our data describe an important role of ROS in HYD1-induced necrotic cell death in MM cells. [Mol Cancer Ther 2009;8(8):2441–51]

Supplemental figures

Supplementary Figures 1-14 from Targeting the Fanconi Anemia/BRCA Pathway Circumvents Drug Resistance in Multiple Myeloma

Supplementary Methods and Materials from Targeting the Fanconi Anemia/BRCA Pathway Circumvents Drug Resistance in Multiple Myeloma

The Fanconi anemia/BRCA (FA/BRCA) DNA damage repair pathway plays a pivotal role in the cellular response to replicative stress induced by DNA alkylating agents and greatly influences drug response in cancer treatment. We recently reported that FA/BRCA genes are overexpressed and causative for drug resistance in human melphalan-resistant multiple myeloma cell lines. However, the transcriptional regulation of the FA/BRCA pathway is not understood. In this report, we describe for the first time a novel function of the NF-κB subunits, RelB/p50, as transcriptional activators of the FA/BRCA pathway. Specifically, our findings point to constitutive phosphorylation of IκB kinase α and subsequent alterations in FANCD2 expression and function as underlying events leading to melphalan resistance in repeatedly exposed multiple myeloma cells. Inhibiting NF-κB by small interfering RNA, blocking the IκB kinase complex with BMS-345541, or using the proteasome inhibitor bortezomib drastically reduced FA/BRCA gene expression and FANCD2 protein expression in myeloma cells, resulting in diminished DNA damage repair and enhanced melphalan sensitivity. Importantly, we also found that bortezomib decreases FA/BRCA gene expression in multiple myeloma patients. These results show for the first time that NF-κB transcriptionally regulates the FA/BRCA pathway and provide evidence for targeting Fanconi anemia–mediated DNA repair to enhance chemotherapeutic response and circumvent drug resistance in myeloma patients. [Cancer Res 2009;69(24):9367–75]

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.

Introduction: In this study, we investigated the prognostic role of the immune cell state atlas in predicting therapeutic benefits of patients treated with immune checkpoint inhibitors (ICI) within the ORIEN network of 18 collaborating cancer centers under the Total Cancer Care protocol. Methods: We utilized RNA-seq data of 926 samples generated from 875 individuals. Gene expression data were deconvoluted for immune cell states using the Carcinoma EcoTyper software. We then conducted a series of survival analyses to test the association between survival outcomes and predicted cell types and states in five malignant tumors: Genitourinary (GU), Gastrointestinal (GI), Thoracic (THO), Cutaneous (CUT), Head & Neck (H&N). The regularized Cox regression model in R package ‘glmnet’ was then applied to select the complementary pathway signatures (including gene ontology and KEGG pathways) to the immune cell states in predicting survival outcomes. We also explored the immune-related long non-coding RNAs (lncRNA) as potential biomarkers for cell states and patient outcomes. Results: EcoTyper analysis revealed that 692 (~80%) of patients were assigned to the 10 pre-identified Carcinoma Ecotypes (CE1 to CE10) or cell state atlas group. Overall, two immune deficiency ecotype patient groups (CE1 and CE2) pre-identified based on the independent training data were linked to worse survival, while two proinflammatory ecotype groups (CE9 and CE10) were associated with favorable surxvival. Those ecotype groups showed strong prognostic significance in predicting OS in melanoma and H&N. Meanwhile, CE6, a non-neoplastic tissue enriched cell subtype, was also found to be highly associated with longer OS in H&N and GU. CE7, an age-related mutation patient subgroup, contributed to shorter survival in both melanoma and GI. We also found that a subset of activated B cell state and the exhausted/effector CD4 T cell state were significantly associated with patient survival in melanoma and GU, respectively. The penalized Cox regression model revealed that β-catenin signaling pathway, P53 pathway and heme metabolism in the MSigDB Hallmark gene sets are the most complementary pathways to the ecotype scores in multiple cancer types. In additional, multiple pathways in KEGG such as endocytosis were found to jointly contribute to the ecotype-pathway composite prognostic model. In anazlying immune-related lncRNA biomarkers, we highlighted the prognostic role of NKILA in our dataset, which has been studied to promote tumor immune evasion. Conclusion: Our analysis has successfully established the utility of immune cell state atlas in predicting therapeutic benefits with ICIs. We expect that the discovered complementary signatures in the cancer-cell compartment will also lead to a novel spectrum of tumor-based biomarkers to ICI. Citation Format: Tingyi Li, Vineeth Sukrithan, Aakrosh Ratan, Martin McCarter, John Carpten, Howard Colman, Alexandra P. Ikeguchi, Igor Puzanov, Susanne Arnold, Michelle Churchman, Patrick Hwu, Paulo C. Rodriguez, William S. Dalton, George J. Weiner, Ahmad Tarhini, Xuefeng Wang. The immune cell state atlas analysis predicts therapeutic benefits with immune checkpoint inhibitors. [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 5703.

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients do 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 involve 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 and histone modifier,PHF19, which featured prominently in several independent models. Lastly, we show that a simple four feature predictor composed of age, International Staging System stage (ISS), and expression ofPHF19andMMSETperforms similarly to more complex models with many more gene expression features included.Key pointsMost comprehensive and unbiased assessment of prognostic biomarkers in MM resulting in a robust and parsimonious model.Identification ofPHF19as the expression based biomarker most strongly associated with rapid progression in MM patients.

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.

2619 Background: Immune checkpoint inhibitors (ICIs) have made significant improvements in the treatment of cancer patients (pts), but many continue to experience primary or secondary resistance. Here, we leveraged clinical and genomic data to identify prognostic biomarkers in pts treated with ICIs utilizing a pan-cancer approach. Methods: Pts were enrolled to the Total Cancer Care protocol across 18 cancer centers within the Oncology Research Information Exchange Network (ORIEN). RNA-seq was performed on tumors following the RSEM pipeline and gene expressions were quantified as Transcript Per Million (TPM) and were logarithmically normalized. An Auto-Encoder Survival Deep Network (AE-SDN) architecture was developed that combined the reconstruction loss of AE with Cox regression for modeling time to event. For comparison, immunoscore for each pt was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells (Galon, 2020) utilizing CIBERSORTx. The quality of overall survival (OS) predictions was assessed using Harrell’s concordance index (C-index). Log-rank test was used to assess stratified group differences (by ICI or cancer histology) along with Kaplan-Meier (KM) survival analysis of AE-SDN and immunoscore. Results: Pts (n=522) with 4 cancer types including melanoma (n=125), renal cell carcinoma (n=149), non-small cell lung cancer (n=128) and head and neck cancer (n=120) treated with 6 ICI regimens were included in this analysis. ICI regimens were nivolumab (n=219), pembrolizumab (n=202), ipilimumab+nivolumab (n=69), ipilimumab (n=30), avelumab (n=1) and cemiplimab (n=1). The Table summarizes the overall C-index and associated 95% CIs and log-rank P values for the entire cohort (regardless of histology) resulting from our proposed AE-SDN model and the separate estimated immunoscore categorization. AE-SDN top selected genes were mostly related to immunity, carcinogenesis and tumor suppression. The corresponding KM plots showed significantly wider separations of the survival curves in favor of our proposed AE-SDN model relative to the immunoscore with more than 20% improvement in prediction power. Conclusions: Deep network machine learning analysis is a promising approach to identifying relevant prognostic biomarkers in cancer pts treated with ICI. This may lead to novel therapeutic predictive signatures and identification of mechanisms of ICI resistance. Our AE-SDN gene expression signature was significantly prognostic and outperformed the estimated CD3+, CD8+ T Cell immunoscore. Further refinements to our prediction power are ongoing along with more advanced neural network architectures to elucidate related functional pathways. [Table: see text]

2618 Background: Despite the significant improvements in treating cancer with immune checkpoint inhibitors (ICIs), many patients (pts) do not achieve disease control. Using Oncology Research Information Exchange Network (ORIEN) Avatar real-world data conducted under the Total Cancer Care protocol we investigate predictive biomarkers of ICI benefits in pts with advanced malignancies. Methods: Clinical data were normalized as part of ORIEN Avatar. RNA-seq was performed on tumor samples following the RSEM pipeline and gene expressions were quantified as Transcript Per Million (TPM). Gene expressions (GE) were log2(TPM+1) transformed. Mann-Whitney U-test was used to compute differences between groups, and Kaplan-Meier survival analysis was performed. Results: Pts (n=1214) with 27 cancer types treated with ICIs were retrieved from the database, where 1143 and 875 patients were profiled by WES and RNA-seq, respectively. 804 pts had both WES and RNA-seq data. The top six cancer types were renal cell carcinoma (n=206), non-small cell lung cancer (n=173), head and neck cancer (n=157), melanoma (n=154), sarcomas (n=99) and bladder cancer (n=87). The ICI regimens included therapy with atezolizumab (n=87), avelumab (n=12), cemiplimab (n=6), ipilimumab (n=47), nivolumab (n=424), pembrolizumab (n=525) and ipilimumab+nivolumab (n=113). Median overall survival (OS) for the entire cohort was 21.9 months. Patients had significant improvement in OS if ICI was given in the first line ( P

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.

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.

Cancer sequencing efforts have demonstrated that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy, one of the most common cancer treatments, is prescribed based on an empiric 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. We have previously developed the Genomic Adjusted Radiation Dose (GARD), a method which accounts for biological heterogeneity and can be utilized to predict optimal RT dose for an individual patient. In this article, we utilize GARD to characterize the biological imprecision of one-size-fits-all RT dosing schemes which result in both over- and under-dosing for the majority of 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 (TCP) and normal tissue complication probabilities (NTCP). This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics. Using data from two prospectively collected cohorts of patients with non-small-cell lung cancer, we validate the competing hazards model by demonstrating that it predicts the results of RTOG 0617. We show how 0617 failure can be explained by the biological imprecision of empiric uniform dose escalation which results in 80% of patients being over-exposed to normal tissue toxicity without potential tumor control benefit. In conclusion, our data reveals a tapestry of radiosensitivity heterogeneity, provides a biological framework that explains the failure of empiric RT dose escalation, and quantifies the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.

Inhibitors that target specific kinases or oncoproteins have become popular additions to or replacements for cytotoxic chemotherapies to treat many different types of cancer. However, many tumors lack a discernable target kinase and an amplified oncoprotein and/or rely on several cooperating mechanisms for progression. Thus, combinations of targeted therapies are essential for treating many cancers to avoid the rapid emergence of resistance. In this issue of the JCI, Ren et al. use an elegant kinase activity–profiling method and identify activity of the oncogene polo-like kinase-1 (PLK1) as an important driver of double-hit lymphoma (DHL), an aggressive subgroup of B cell lymphoma characterized by chromosomal translocations involving c-MYC and BCL2 or BCL6. Moreover, PLK1 activity was associated with MYC expression and poor prognosis in DHL patients. PLK1 inhibition with volasertib, alone and in combination with the BCL-2 inhibitor venetoclax, was efficacious in multiple DHL models, including mice harboring DHL patient–derived xenografts. Together, these data support PLK1 as a promising prognostic marker and therapeutic target for DHL.

Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Ex vivo Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r = 0.5658, P < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two in silico clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma. Cancer Res; 77(12); 3336–51. ©2017 AACR.

Purpose/Objective(s): Although radiotherapy (RT) remains a critical curative agent for cancer, it has yet to adapt a biological basis in the clinic. We previously proposed the gene expression-based radiosensitivity index (RSI) and the genomic-adjusted radiation dose (GARD), as a clinically-feasible approach to biology-based RT. We hypothesize that empiric one-size fits all RT dose is biologically imprecise and negatively impacts clinical outcome. Materials/Methods: We utilize the RSI/GARD formalism to calculate a personalized RT prescription dose that biologically optimizes prescription dose for each patient (RxRSI). We integrated RxRSI into a commercial treatment planning system to create the first approach to personalized biologically-optimized RT plans. We compare RxRSI with standard of care empiric radiotherapy dose in 2 cohorts of NSCLC patients (n=60 and n=1,747) from an IRB-approved de-identified institutional biorepository. To quantify the opportunity inherent in personalized RT prescription, we develop a first-in kind precision RxRSI model that estimates local control (LC) based on whether an optimal RT dose (RxRSI) is delivered and penalizes outcome based on exposure to excess normal tissue complication risk (penalized local control, pLC). To validate the precision RxRSI model, we ran an in silico clinical trial similar to RTOG 0617 in the modeling cohort. Results: We demonstrate that up to 75% of patients receive non-optimal radiotherapy doses, when treated with a one-size fits all empiric approach. Conversely, we show that personalized, radiotherapy prescription dose delivers optimal doses to up to 75% of the patients even when restricted to doses within the standard of care. These differences result in quantifiable excess doses to normal tissue that negatively impact clinical outcome. We validate the precision RxRSI model by demonstrating that it predicts that empiric radiotherapy dose escalation to 74 Gy results in no improvement in radiotherapy-associated outcome (pLC) as demonstrated in RTOG 0617. The precision RxRSI model demonstrates that only an additional 16.2% of lung cancer patients achieve RxRSI by receiving 74 Gy (beyond 60Gy). A genomic-based strategy to deliver 74 Gy to only those patients could have produced a 6.3% absolute improvement in clinical outcome (pLC) for the whole population (p

The few studies that have examined rates of acute myeloid leukemia (AML) transformation in lenalidomide‐treated myelodysplastic syndrome (MDS) patients have been limited to deletion 5q MDS. The association between lenalidomide and subsequent primary malignancies (SPMs) in MDS patients has not been evaluated previously. We conducted a retrospective cohort study to evaluate the risk of both SPM and AML in association with lenalidomide. A cohort of MDS patients (n = 1248) treated between 2004 and 2012 at Moffitt Cancer Center were identified, and incident cases of SPM and AML transformation were ascertained. Using a nested case–control design, MDS controls were 1:1 matched to SPM (n = 41) and AML (n = 150) cases, on age and date of MDS diagnosis, gender, follow‐up time, IPSS, and del (5q). Associations between lenalidomide and (1) SPM incidence and (2) AML transformation were estimated with hazards ratios (HR) and 95% confidence intervals (CIs) in the cohort and odds ratios (OR) in the case–control analysis. SPM incidence did not differ significantly between cohort MDS patients treated with (0.7 per 100 person‐years) or without lenalidomide (1.4 per 100 person‐years) (HR = 1.04, 95% CI = 0.40–2.74), whereas a significantly reduced SPM risk was observed in the case–control sample (OR = 0.03, 95% CI =

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.

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.

To advance medicine toward better evidence-based, cost-effective and individualized treatment, a new model of discovery, translation and delivery of information must be developed. This requires the collaboration of the major constituents in the areas of healthcare (i.e., patients, clinicians, administrators and researchers) and the partnership of disciplines (i.e., bioinformatics, epidemiologists, statisticians, information technologists, physicians and scientists) and organizations (i.e., drug and device companies, healthcare agencies, academic and community medical centers and information technology firms) to develop an integrative platform. The over-riding goal of this platform is to improve patient care, with the developed system enabling this by providing each of the major constituents with evidence-based and tailored information at the individual patient level.

B-cell tumorigenesis results from a host of known and unknown genetic anomalies, including non-random translocations of genes that normally function as determinants of cell proliferation or cell survival to regions juxtaposed to active immunoglobulin heavy chain enhancer elements, chromosomal aneuploidy, somatic mutations that further affect oncogenic signaling and loss of heterozygosity of tumor-suppressor genes. However, it is critical to recognize that even in the setting of a genetic disease, the B-cell/plasma cell tumor microenvironment (TME) contributes significantly to malignant transformation and pathogenesis. Over a decade ago, we proposed the concept of cell adhesion-mediated drug resistance to delineate a form of TME-mediated drug resistance that protects hematopoietic tumor cells from the initial effect of diverse therapies. In the interim, it has been increasingly appreciated that TME also contributes to tumor initiation and progression through sustained growth/proliferation, self-renewal capacity, immune evasion, migration and invasion as well as resistance to cell death in a host of B-cell malignancies, including mantle cell lymphoma, diffuse large B-cell lymphoma, Waldenstroms macroglobulinemia, chronic lymphocytic leukemia and multiple myeloma. Within this review, we propose that TME and the tumor co-evolve as a consequence of bidirectional signaling networks. As such, TME represents an important target and should be considered integral to tumor progression and drug response.

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.

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-β1 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., Ibrutinib has demonstrated high response rates in B-cell lymphomas but a lot of ibrutinib-treated patients relapse with resistance. This study unified TME-mediated de novo and acquired drug resistance through B-cell receptor signalling and PI3K-AKT-mTOR axis and provides a combination therapeutic strategy against B-cell malignancies.

Summary 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.

Background Senescence-associated genes (SAGs) are responsible for the senescence-associated secretory phenotype, linked in turn to cellular aging, the aging brain, and the pathogenesis of cancer. Objective We hypothesized that senescence-associated genes are overexpressed in older patients, in higher grades of glioma, and portend a poor prognosis. Methods Forty-seven gliomas were arrayed on a custom version of the Affymetrix HG-U133+2.0 GeneChip, for expression of fourteen senescence-associated genes: CCL2 , CCL7 , CDKN1A , COPG , CSF2RB , CXCL1 , ICAM - 1 , IGFBP -3, IL - 6 , IL - 8 , SAA4 , TNFRSF - 11B , TNFSF - 11 and TP53 . A combined "senescence score" was generated using principal component analysis to measure the combined effect of the senescence-associated gene signature. Results An elevated senescence score correlated with older age (r=0.37; P =.01) as well as a higher degree of malignancy, as determined by WHO, histological grade (r=0.49; P P =.06). Gliosarcomas showed the highest scores. Six genes independently correlated with either age ( IL - 6 , TNFRSF - 11B , IGFBP - 3 , SAA4 , and COPG ), prognosis ( IL - 6 , SAA4 ), or the grade of the glioma ( IL - 6 , IL - 8 , ICAM - 1 , IGFBP - 3 , and COPG ). Conclusion We report: 1) a novel molecular signature in human gliomas, based on cellular senescence, translating the concept of SAG to human cancer; 2) the senescence signature is composed of genes central to the pathogenesis of gliomas, defining a novel, aggressive subtype of glioma; and 3) these genes provide prognostic biomarkers, as well as targets, for drug discovery and immunotherapy.

Background: Two strategies to interrogate the insulin growth factor 1 receptor (IGF-1R) pathway were investigated: vertical inhibition with dalotuzumab and MK-2206 or ridaforolimus to potentiate PI3K pathway targeting and horizontal cross-talk inhibition with dalotuzumab and MK-0752 to exert effects against cellular proliferation, angiogenesis, and stem cell propagation. Methods: A phase I, multi-cohort dose escalation study was conducted in patients with advanced solid tumours. Patients received dalotuzumab (10 mg kg–1) and escalating doses of MK-2206 (90–200 mg) or escalating doses of dalotuzumab (7.5–10 mg kg–1) and MK-0752 (1800 mg) weekly. Upon maximum tolerated dose determination, patients with low-RAS signature, high-IGF1 expression ovarian cancer were randomised to dalotuzumab/MK-2206 versus dalotuzumab/ridaforolimus, whereas patients with high IGF1/low IGF2 expression colorectal cancer received dalotuzumab/MK-0752. Results: A total of 47 patients were enrolled: 29 in part A (18 in the dalotuzumab/MK-2206 arm and 11 in the dalotuzumab/MK-0752 arm) and 18 in part B (6 in each arm). Dose-limiting toxicities (DLTs) for dalotuzumab/MK-2206 included grade 4 neutropenia and grade 3 serum sickness-like reaction, maculopapular rash, and gastrointestinal inflammation. For dalotuzumab/MK-0752, DLTs included grade 3 dehydration, rash, and diarrhoea. Seven patients remained on study for >4 cycles. Conclusions: Dalotuzumab/MK-2206 and dalotuzumab/MK-0752 combinations were tolerable. Further developments of prospectively validated predictive biomarkers to aid in patient selection for anti-IGF-1R therapies are needed.

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. Clin Cancer Res; 20(5); 1081–6. ©2014 AACR.

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/m2 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. Am. J. Hematol. 89:62–67, 2014. © 2013 Wiley Periodicals, Inc.

Problem: Multiple myeloma (MM) is a treatable yet incurable hematologic cancer that lacks predictive biomarkers. Approach: Here we apply a systems biology approach to determine patient-specific mechanisms, as well as signatures of drug resistance in MM. To achieve this goal, we have combined ex vivo drug sensitivity data from 307 MM fresh primary samples tested with 162 drugs and combinations, with paired molecular data (RNAseq and mutational profiling) from a larger overlapping cohort of 606 MM samples from Moffitt's Multiple Myeloma Working Group (MMWG) repository in collaboration with M2Gen/Oncology Research Information Exchange Network (ORIEN). With the purpose of decoupling biological function from intracellular control mechanisms, we have re-constructed a MM-specific transcriptional regulatory network composed of clusters of co-expressing genes. We demonstrate how this gene cluster network regulates biology, and how different biological functions (e.g. Proteasome, Ribosome, Oxidative Phosphorylation) share common regulatory circuits. We have used gene set enrichment analysis (GSEA) to identify gene clusters with transcriptional profiles, and investigated mutations associated with drug resistance. Results: As a preliminary validation of this approach, we have confirmed established mechanisms of resistance (MOR) to targeted therapies, as well as proposed novel MOR to clinically relevant and experimental drugs in MM, as well as putative synergistic drug combinations. In addition, we have identified a list of low frequency mutations ( We have also explored evolution of drug resistance in sequential samples. Consistent with altered transcriptional programming in therapeutic escape, single sample GSEA demonstrated cumulative dysregulation of cancer-related genes with increasing lines of therapy. We have identified 60 MM-specific transcriptional core auto-regulatory circuits (CRC) correlated with ex vivo drug resistance, suggesting that characterization of transcriptional regulatory circuits is a critical approach to infer mechanisms of MM resistance, and providing a novel rationale for combination therapy. We hypothesized that identifying and targeting these transcriptional CRCs could facilitate patient-specific rational combination therapies, with the goal to overcome therapy resistance in the clinic. As proof-of-principle, we have identified a novel transcriptional network consisting of 3 of these CRCs (FOXP1, JUNB and JUN) associated with BCL2 inhibitor (BCL2i) response in MM. Our preliminary data suggests that this transcriptional regulatory circuit is associated to t(11;14) MM through CCND1 up-regulation, but is also present in non-t(11;14) BCL2i-sensitive primary samples, and can be modulated to induce BCL2i sensitivity in non-t(11;14) MM through HDAC inhibitors. Conclusion and next steps: Preliminary results confirm the potential of this combination of unsupervised and supervised, yet functionally testable approach, to infer novel, and patient-specific MOR for MM drugs. Disclosures Dai: M2Gen: Employment. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Shain:Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Adaptive Biotechnologies: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

Over the last decade we have witnessed an explosion in the number of therapeutic options available to patients with multiple myeloma (MM). In spite of the marked improvements in patient outcomes paralleling these approvals, MM remains an incurable malignancy for the vast majority of patients following a course of therapeutic successes and failures. As such, there remains a dire need to develop new tools to improve the management of MM patients. A number of groups are leading efforts to combine big data and artificial intelligence to better inform patient care via precision medicine. At Moffitt, in collaboration with the M2Gen/ORIEN (Oncology Research Information Exchange Network), we have begun to accumulate big data in MM. Patients opt in to (consent) for collection of rich clinical data (demographics, staging, risk, complete disease course treatment data) and in the setting of bone marrow biopsy the allocation of CD138-selected cells for molecular analysis (whole exome sequencing (WES) and RNA sequencing as well as peripheral blood mononuclear cells for WES). To date, we have collected over 1000 samples for over 800 individual patients with plasma cell disorders. In the setting of oncology, the ultimate goal of model will be selection of ideal treatments. We expect that AI analysis may validate of patient response to treatments and enable cohort selection, as real patient cohorts can be selected from those predicted by the model. One approach is to utilize reinforcement learning (RL). In RL, the algorithm attempts to learn actions to optimize a type action a defined state and weight any tradeoffs for maximal reward. Our initial utilization of RL involved a relatively small cohort of 402 patients with treatment medication data. This encompassed 1692 lines of treatment with a mean of 4.21 lines of therapy per patient (Median of 4 lines per patient). This included 132 combinations of 22 myeloma therapeutics. The heterogeneity in treatment is highlighted by the fact that no pathways overlap after line 4. Each Q-value in Q-table is the current reward for an action in a state plus the discounted anticipated future reward for taking that action. Iteration helps you converge on the actual values for the future reward (can be model-free). The end result is a policy, P(s), that tells you what the ideal action is at state. There are a near infinite number of possible states, considering treatment history, age, GEP, cytogenetics, comorbidities, staging and others. We presume that action makes intuitive sense as medication (treatment) only and that reward should be some form of treatment response. We have begun the iterative process of trying different state and reward functions. Median imputation shows 5% improvement in response accuracy over listwise, but median imputation throws off practical accuracy in a binary reward case. While we found that the exercise has great potential. We found that there are possible improvements (multiple imputation). We will need to expand covariate analysis. Combinatorics need to be considered in machine learning in medium-sized data sets. Model-free machine learning is limited on medium-sized data. As such, combined resources and/or utilization of large networks such as ORIEN will be critical for the successful integration of RL or other AI tools in MM. We also learned that adding variables to the model doesn't necessarily increase accuracy. Future work will involve continued application of alternate state/reward functions. Loosen iQ-learning framework to allow for better covariate selection for state/reward functions. Improve imputation techniques to include more covariates and have more certainty in model accuracy. We may also refine accuracy metric to allow for prediction of bucketed response and temporal disease burden (M-spike vs. time). Updated data on a larger cohort will be presented at the annual meeting. Disclosures Shain: Adaptive Biotechnologies: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Dai:M2Gen: Employment. Nishihori:Novartis: Research Funding; Karyopharm: Research Funding. Brayer:Janssen: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau. Alsina:Bristol-Myers Squibb: Research Funding; Janssen: Speakers Bureau; Amgen: Speakers Bureau. Baz:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Research Funding; Merck: Research Funding; Sanofi: Research Funding; Bristol-Myers Squibb: Research Funding. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria.

Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

Summary 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.

Risk of subsequent primary malignancies (SPMs) associated with lenalidomide therapy in multiple myeloma (MM) patients, outside the context of melphalan-based therapy is not established. We assessed the risk of SPMs in lenalidomide treated MM patients (n = 1653) at Moffitt Cancer Center (2004-2012) outside the context of melphalan-based induction therapy and post-melphalan maintenance therapy, via (1) cohort analysis and (2) nested case-control study. Incident SPMs (n = 51) were matched to controls (n = 102) on age at MM diagnosis, gender, follow-up time, and date of diagnosis. Incidence of SPM differed significantly (p = 0.0038) between MM patients treated with and without lenalidomide (5-year incidence estimates of 3.2 and 6.2%, respectively), although not significant after adjustment for age and year of diagnosis (HR = 0.82, 95%CI = 0.43-1.57). Lenalidomide treatment was inversely associated with SPM in the nested case-control analysis (OR = 0.03, 95%CI = 0.002-0.34). In this large cohort of MM patients, lenalidomide treatment was not associated with an increased risk of SPM.

Summary We investigated the transcriptional and epigenetic repression of miR-29 by MYC , HDAC3 , and EZH2 in mantle cell lymphoma and other MYC -associated lymphomas. We demonstrate that miR-29 is repressed by MYC through a corepressor complex with HDAC3 and EZH2. MYC contributes to EZH2 upregulation via repression of the EZH2 targeting miR-26a, and EZH2 induces MYC via inhibition of the MYC targeting miR-494 to create positive feedback. Combined inhibition of HDAC3 and EZH2 cooperatively disrupted the MYC-EZH2-miR-29 axis, resulting in restoration of miR-29 expression, downregulation of miR-29-targeted genes, and lymphoma growth suppression in vitro and in vivo. These findings define a MYC-mediated miRNA repression mechanism, shed light on MYC lymphomagenesis mechanisms, and reveal promising therapeutic targets for aggressive B-cell malignancies.

The National Cancer Institute’s Center to Reduce Cancer Health Disparities has created pilot training opportunities under the “Continuing Umbrella of Research Experiences” (CURE) program that focus on emerging technologies (ET). In this pilot project, an eighteen 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.

We conducted a Phase 1/2 study of bortezomib administered in combination with high-dose melphalan followed by tandem autologous transplants in patients with primary resistant multiple myeloma. Thirty patients received two cycles of salvage bortezomib followed by stem cell mobilization with granulocyte colony-stimulating factor and harvest. Melphalan 100 mg/m(2) per day on two consecutive days was administered, immediately followed by one dose of bortezomib (dose escalation) and stem cell infusion. The median beta 2-microglobulin was 4·35 mg/l (range: 1·8-11·4); albumin was 37 g/l (range: 3·1-4·9); high-risk karyotypes were noted in 45% of patients. The maximum planned dose of bortezomib at 1·3 mg/m(2) was well tolerated and a formal maximum tolerated dose was not determined. The peak of best overall response (≥partial response) and complete response rates after tandem transplants were 84% and 36%, respectively. With a median follow-up of 48 months, the median progression-free survival was 15 [95% confidence interval (CI): 11-21] months and the median overall survival was 35 (95% CI: 22-43) months. Correlative studies demonstrated decreased expression of BRCA2 (P = 0·0072) and FANCF (P = 0·0458) mRNA following bortezomib treatment. Bortezomib combined with high-dose melphalan is a well-tolerated conditioning with some activity in patients with resistant myeloma.

Purpose: The Quantitative Assay Database (QuAD), http://proteome.moffitt.org/QUAD/, facilitates widespread implementation of quantitative mass spectrometry in cancer biology and clinical research through sharing of methods and reagents for monitoring protein expression and modification. Experimental design: Liquid chromatography coupled to multiple reaction monitoring (LC-MRM) mass spectrometry assays are developed using SDS-PAGE fractionated lysates from cancer cell lines. Pathway maps created using GeneGO Metacore provide the biological relationships between proteins and illustrate concepts for multiplexed analysis; each protein can be selected to examine assay development at the protein and peptide levels. Results: The coupling of SDS-PAGE and multiple reaction monitoring mass spectrometry screening has been used to detect 876 peptides from 218 cancer-related proteins in model systems including colon, lung, melanoma, leukemias, and myeloma, which has led to the development of 95 quantitative assays including stable-isotope-labeled peptide standards. Methods are published online and peptide standards are made available to the research community. Protein expression measurements for heat shock proteins, including a comparison with ELISA and monitoring response to the HSP90 inhibitor, 17-(dimethylaminoethylamino)-17-demethoxygeldanamycin (17-DMAG), are used to illustrate the components of the QuAD and its potential utility. Conclusions and Clinical Relevance: This resource enables quantitative assessment of protein components of signaling pathways and biological processes and holds promise for systematic investigation of treatment responses in cancer.

The Patient Protection and Affordable Care Act of 2010 will have a profound influence on health care in the United States, including how we conduct cancer research and cancer care delivery. For this reason, oncologists and researchers must be intimately involved in the implementation and interpretation of this important legislation. A major goal of the Act is to improve access to affordable, quality health care. An important element in achieving this goal will be to learn from patients' experiences and build the foundation for evidence-based personalized medicine. This will require a partnership among researchers, clinicians, policy makers and regulators, and patients to design an integrated information network system that will be the basis for providing the right treatment for the right patient in the right place at the right time. In this review, we will discuss the salient points of the Act that specifically affect cancer research and care, as well as highlight opportunities for oncologists and researchers to play a primary role in developing a health care system that includes personalized medicine approaches that will in turn enhance the likelihood of achieving the goals and objectives of the health care reform act. Clin Cancer Res; 16(24); 5987–96. ©2010 AACR.

Background: c-MYC is a transcription factor that promotes oncogenesis by activating and repressing its target genes that control cell growth, metabolism, and proliferation. MYC is deregulated in a large proportion of aggressive B-cell lymphomas. A typical example is the Double-Hit Lymphoma (DHL) and Double-Expression Lymphoma (DEL) which present with a rapidly progressing clinical course, refractory to treatment, poor clinical outcome, and currently considered incurable. Nevertheless, MYC is considered as an "undruggable" target since it has no "active site" amenable to binding by conventional small molecule inhibitors. Moreover, MYC has a broad spectrum of functions in cell proliferation, survival, metabolism, and others, so direct inhibition would likely cause severe side effects. Besides direct inhibition, another practical strategy is to target druggable proteins that are essential for the viability of MYC-driven tumors, inducing MYC-dependent "synthetic lethality". The advantage of such approach is a capability of killing tumor cells discriminately, while leaving non-tumor cells intact or less influenced. This study is designed to identify such targets and explore practical novel strategies to treat MYC-driven lymphomas, especially DHL/DEL. Methods and Results: By integrating activity-based proteomic profiling and drug screens in isogenic MYC on/off lymphoma cells, we identified polo-like kinase-1 (PLK1) as an essential regulator of the MYC-dependent kinome in DHL/DEL. Notably, PLK1 was expressed at high levels in DHL, correlated with MYC expression and connoted poor outcome. Further, PLK1 is directly activated by MYC on transcriptional level and in turn, PLK1 signaling augmented MYC protein stability by promoting its phosphorylation and suppressing its degradation. Thus, MYC and PLK1 form a feed-forward circuit in lymphoma cells. Finally, both in vitro and in vivo studies demonstrated that inhibition of PLK1 triggered degradation of MYC and of the anti-apoptotic protein MCL1, and PLK1 inhibitors showed synergy with BCL-2 antagonists in blocking DHL/DEL cell growth, survival, and tumorigenicity. These data support that PLK1 is a promising therapeutic target in MYC-driven lymphomas. Brief summary: Functional pharmacoproteomics identified PLK1 as a therapeutic vulnerability for MYC-driven lymphoma, which was a synthetic lethal for DHL/DEL when targeted with BCL-2 inhibitors. Disclosures Vose: Roche: Honoraria; Merck Sharp & Dohme Corp.: Research Funding; Acerta Pharma: Research Funding; Seattle Genetics, Inc.: Research Funding; Novartis: Honoraria, Research Funding; Kite Pharma: Research Funding; Bristol Myers Squibb: Research Funding; Epizyme: Honoraria; Legend Pharmaceuticals: Honoraria; Abbvie: Honoraria; Celgene: Research Funding; Incyte Corp.: Research Funding.