Faculty Bibliography

Promising new drugs are being evaluated for treatment of multiple myeloma (MM), but their impact should be measured against the expected outcome in patients failing current therapies. However, the natural history of relapsed disease in the current era remains unclear. We studied 286 patients with relapsed MM, who were refractory to bortezomib and were relapsed following, refractory to or ineligible to receive, an IMiD (immunomodulatory drug), had measurable disease, and ECOG PS of 0, 1 or 2. The date patients satisfied the entry criteria was defined as time zero (T(0)). The median age at diagnosis was 58 years, and time from diagnosis to T(0) was 3.3 years. Following T(0), 213 (74%) patients had a treatment recorded with one or more regimens (median=1; range 0-8). The first regimen contained bortezomib in 55 (26%) patients and an IMiD in 70 (33%). A minor response or better was seen to at least one therapy after T(0) in 94 patients (44%) including ≥ partial response in 69 (32%). The median overall survival and event-free survival from T(0) were 9 and 5 months, respectively. This study confirms the poor outcome, once patients become refractory to current treatments. The results provide context for interpreting ongoing trials of new drugs.

Next generation DNA sequencing (NGS) technologies have revolutionized the pace at which whole genome and exome sequences can be generated. However, despite these advances, many of the methods for targeted resequencing, such as the generation of high-depth exome sequences, are somewhat limited by the relatively large amounts of starting DNA that are normally required. In the case of tumour analysis this is particularly pertinent as many tumour biopsies often return submicrogram quantities of DNA, especially when tumours are microdissected prior to analysis. Here, we present a method for exome capture and resequencing using as little as 50 ng of starting DNA. The sequencing libraries generated by this minimal starting amount (MSA-Cap) method generate datasets that are comparable to standard amount (SA) whole exome libraries that use three micrograms of starting DNA. This method, which can be performed in most laboratories using commonly available reagents, has the potential to enhance large scale profiling efforts such as the resequencing of tumour exomes.

Recent advances in multiple myeloma (MM) therapy have led to significantly longer median survival rates and some patients being cured. At the same time, our understanding of MM biology and the molecular mechanisms driving the disease is constantly improving. Next-generation sequencing technologies now allow insights into the genetic aberrations in MM at a genome-wide scale and across different developmental stages in the course of an individual tumor. This improved knowledge about MM biology needs to be rapidly translated and transformed into diagnostic and therapeutic applications to finally achieve cure in a larger proportion of patients. As a part of these translational efforts, novel drugs that inhibit oncogenic proteins overexpressed in defined molecular subgroups of the disease, such as FGFR3 and MMSET in t(4;14) MM, are currently being developed. The potential of targeted next-generation diagnostic tests to rapidly identify clinically relevant molecular subgroups is being evaluated. The technical tools to detect and define tumor subclones may potentially become clinically relevant because intraclonal tumor heterogeneity has become apparent in many cancers. The emergence of different MM subclones under the selective pressure of treatment is important in MM, especially in the context of maintenance therapy and treatment for asymptomatic stages of the disease. Finally, novel diagnostic and therapeutic achievements have to be implemented into innovative clinical trial strategies with smaller trials for molecularly defined high-risk patients and large trials with a long follow-up for the patients most profiting from the current treatment protocols. These combined approaches will hopefully transform the current one-for-all care into a more tailored, individual therapeutic strategy for MM patients.

A 55-year-old man without prior medical problems presented to the emergency room with a 2-month history of progressive fatigue. Initial workup showed a hemoglobin level of 9.0 g/dL and an increased total protein level of 12.6 g/dL. Further testing revealed a normal serum calcium and creatinine, an IgG kappa paraprotein of 5.4 g/dL, and a Bence Jones proteinuria of 450 mg of kappa light chain in 24 hours. The bone marrow showed 60% plasma cells with normal cytogenetics and t(4;14), but no other associated genetic abnormalities by FISH. Skeletal survey and MRI of the spine were negative for myeloma bone disease. The patient's oncologist recently began therapy with lenalidomide, bortezomib, and dexamethasone and recommended the initiation of zoledronic acid monthly. The patient questions the benefit of this drug because he has no bone disease.

Myeloma bone disease (BD) not only impairs quality of life, but is also associated with impaired survival. Studies of the biology underlying BD support the notion that the increased osteoclastogenesis and suppressed osteoblastogenesis is both a consequence and a necessity for tumour growth and clonal expansion. Survival and expansion of the myeloma clone are dependent on its interactions with bone elements; thus, targeting these interactions should have anti-myeloma activities. Indeed, both experimental and clinical findings indicate that bone-targeted therapies, not only improve BD, but also create an inhospitable environment for myeloma cell growth and survival, favouring improved clinical outcome. This chapter summarizes recent progress in our understandings of the biology of myeloma BD, highlighting the role of osteoclasts and osteoblasts in this process and how they can be targeted therapeutically. Unravelling the mechanisms underlying myeloma-bone interactions will facilitate the development of novel therapeutic agents to treat BD, which as a consequence are likely to improve the clinical outcome of myeloma patients.

OBJECTIVE:In the Medical Research Council Myeloma IX Study (MMIX), zoledronic acid (ZOL) 4 mg 3-4/week reduced the incidence of skeletal-related events (SREs), increased progression free survival (PFS), and prolonged overall survival (OS), compared with clodronic acid (CLO) 1600 mg daily, in 1970 patients with newly-diagnosed multiple myeloma (MM). METHODS:An economic model was used to project PFS, OS, the incidence of SREs and adverse events and expected lifetime healthcare costs for patients with newly-diagnosed MM who are alternatively assumed to receive ZOL or CLO. The incremental cost-effectiveness ratio [ICER] of ZOL vs CLO was calculated as the ratio of the difference in cost to the difference in quality-adjusted life years (QALYs). Model inputs were based on results of MMIX and published sources. RESULTS:Compared with CLO, treatment with ZOL increases QALYs by 0.30 at an additional cost of £1653, yielding an ICER of £5443 per QALY gained. If the threshold ICER is £20,000 per QALY, the estimated probability that ZOL is cost-effective is 90%. LIMITATIONS/CONCLUSIONS:The main limitation of this study is the lack of data on the effects of zoledronic acid on survival beyond the end of follow-up in the MMIX trial. However, cost-effectiveness was favourable even under the highly conservative scenario in which the timeframe of the model was limited to 5 years. CONCLUSIONS:Compared with clodronic acid, zoledronic acid represents a cost-effective treatment alternative in patients with multiple myeloma.

PURPOSE/OBJECTIVE:Regions on 1p with recurrent deletions in presenting myeloma patients were examined with the purpose of defining the deletions and assessing their survival impact. EXPERIMENTAL DESIGN/METHODS:Gene mapping, gene expression, FISH, and mutation analyses were conducted on patient samples from the MRC Myeloma IX trial and correlated with clinical outcome data. RESULTS:1p32.3 was deleted in 11% of cases, and deletion was strongly associated with impaired overall survival (OS) in patients treated with autologous stem cell transplant (ASCT). In patients treated less intensively, del(1)(p32.3) was not associated with adverse progression-free survival (PFS) or OS. The target of homozygous deletions was CDKN2C, however its role in the adverse outcome of cases with hemizygous deletion was less certain. 1p22.1-21.2 was the most frequently deleted region and contained the candidate genes MTF2 and TMED5. No mutations were identified in these genes. 1p12 was deleted in 19% of cases, and deletion was associated with impaired OS in univariate analysis. The target of homozygous deletion was FAM46C, which was mutated in 3.4% of cases. When cases with FAM46C deletion or mutation were considered together, they were strongly associated with impaired OS in the intensive treatment setting. CONCLUSION/CONCLUSIONS:Deletion of 1p32.3 and 1p12 was associated with impaired OS in myeloma patients receiving ASCT. FAM46C was identified as a gene with potential pathogenic and prognostic significance based on the occurrence of recurrent homozygous deletions and mutations.

PURPOSE/OBJECTIVE:The combined use of microarray technologies and bioinformatics analysis has improved our understanding of biological complexity of multiple myeloma (MM). In contrast, the application of the same technology in the attempt to predict clinical outcome has been less successful with the identification of heterogeneous molecular signatures. Herein, we have reconstructed gene regulatory networks in a panel of 1,883 samples from MM patients derived from publicly available gene expression sets, to allow the identification of robust and reproducible signatures associated with poor prognosis across independent data sets. EXPERIMENTAL DESIGN/METHODS:Gene regulatory networks were reconstructed by using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) and microarray data from seven MM data sets. Critical analysis of network components was applied to identify genes playing an essential role in transcriptional networks, which are conserved between data sets. RESULTS:Network critical analysis revealed that (i) CCND1 and CCND2 were the most critical genes; (ii) CCND2, AIF1, and BLNK had the largest number of connections shared among the data sets; (iii) robust gene signatures with prognostic power were derived from the most critical transcripts and from shared primary neighbors of the most connected nodes. Specifically, a critical-gene model, comprising FAM53B, KIF21B, WHSC1, and TMPO, and a neighbor-gene model, comprising BLNK shared neighbors CSGALNACT1 and SLC7A7, predicted survival in all data sets with follow-up information. CONCLUSIONS:The reconstruction of gene regulatory networks in a large panel of MM tumors defined robust and reproducible signatures with prognostic importance, and may lead to identify novel molecular mechanisms central to MM biology.

Myeloma bone disease (BD) not only impairs quality of life, but is also associated with impaired survival. Studies of the biology underlying BD support the notion that the increased osteoclastogenesis and suppressed osteoblastogenesis, is both a consequence and a necessity for tumour growth and clonal expansion. Survival and expansion of the myeloma clone is dependent on its interactions with bone elements, thus targeting these interactions should have antimyeloma activities. Indeed both experimental and clinical findings indicate that bone-targeted therapies not only improve BD, but also create an inhospitable environment for myeloma cell growth and survival, favouring improved clinical outcome. This review summarizes recent progress in our understandings of the biology of myeloma BD, highlighting the role of osteoclasts and osteoblasts in this process and how they can be targeted therapeutically. Unravelling the mechanisms underlying myeloma-bone interactions will facilitate the development of novel therapeutic agents to treat BD, which as a consequence are likely to improve the clinical outcome of myeloma patients.

To identify risk variants for multiple myeloma, we conducted a genome-wide association study of 1,675 individuals with multiple myeloma and 5,903 control subjects. We identified risk loci for multiple myeloma at 3p22.1 (rs1052501 in ULK4; odds ratio (OR) = 1.32; P = 7.47 × 10(-9)) and 7p15.3 (rs4487645, OR = 1.38; P = 3.33 × 10(-15)). In addition, we observed a promising association at 2p23.3 (rs6746082, OR = 1.29; P = 1.22 × 10(-7)). Our study identifies new genomic regions associated with multiple myeloma risk that may lead to new etiological insights.