Title: Investigating interactions between the triad of (I) Myeloma disease activity, (II) Immune-competence (iii) Infections and Health Care Associated Infections to stratify patients for risk of infection, guide use of prophylactic antibiotics and improve response to anti-myeloma therapies
Funder: Department of Health and Social Care

Abstract: Multiple myeloma (MM) is a cancer of antibody secreting plasma cells in bone marrow. MM causes anaemia, lytic bone lesions and fractures, kidney damage and severe bacterial infections resultant of profound immunodeficiency. There are 4,500 new UK MM cases per year, and in the first 12 weeks 30% will have a serious bacterial infection (1/6th of these will die). Antibiotic prophylaxis is an obvious strategy to prevent infection, hospital admission and early death but ambivalence to its use exists because of the risks of mounting antibiotic-resistance and Health Care Associated Infections (HCAI). TEAMM is assessing risks, benefits and health economics of levofloxacin once daily for 12 weeks (antibiotic prophylaxis) in newly diagnosed MM by a prospective, multi-centre, randomized, double-blind, placebo-controlled trial. Interactions between the triad of MM disease activity, immunity and infection are central to TEAMM which aims to prevent infection. Active MM profoundly suppresses both innate immunity including phagocyte function and specific immunity including antibody production, causing a heavy burden of infection associated morbidity and mortality. Additionally, infections delay administration of anti-MM therapy and inflammation associated with infection is thought to nurture MM activity and resistance to anti-MM therapy. TEAMM is collecting five paired serum and urine samples from 800 patients, from entry to 16-weeks post-diagnosis. TEAMM is funded for only limited laboratory studies; this proposal will extend those studies by analyses of established biomarkers associated with the triad of (I) immune-competence, (II) infection-induced inflammation and (III) MM disease activity. (I) Established biomarkers of immune-competence We aim to refine risk-stratification of patients for infection so that the decision to prescribe antibiotic prophylaxis can be personalised. Patients with better immune-competence may have a lower risk of infection including HCAI, faster and deeper responses to anti-MM therapy, and thus, no net-benefit from daily levofloxacin; they may be better managed by infection education and prompt antibiotics early in infection. (II) Conventional biomarkers of infection-induced inflammation Infection diagnosis is dependent on identifying immune responses to infection (e.g., fever), yet these responses are attenuated in immune-deficient MM patients, and further blunted by anti-MM therapies especially steroids. To improve infection diagnosis in MM, a better understanding of the interaction between infection and incompetent immune systems is required. Further, the measurement of biomarkers of infection-induced inflammation throughout the 12 weeks of TEAMM will enable correlation with MM disease activity giving us the power to assess the importance of infection-induced inflammation in nurturing MM survival. (III) Sensitive biomarkers of myeloma disease activity Improved measurement of MM disease activity and its response to anti-MM therapy will facilitate this investigation into the relationship between inflammation and MM activity. It will also enable earlier identification of poor responders who may be rescued by alternative therapies thus: (a) reduce duration at risk of infection and other MM associated morbidities; (b) prevent early MM progression through new targeting of resistant disease; and (c) enable more economical treatment stratification as inadequate anti-MM therapy is expensive.

Lay abstract: Myeloma is a cancer affecting four thousand new patients in the UK each year. The cancer has a number of side-effects, including severe damage to the immune system. As a result, one-third of patients will have a serious infection in the first three months after diagnosis, and this can often lead to death. The ongoing NIHR-funded TEAMM trial is measuring whether antibiotics are able to reduce infections and death in newly-diagnosed myeloma patients. The TEAMM trial is collecting samples from eight-hundred patients, starting at myeloma diagnosis and finishing four months after diagnosis. The TEAMM trial is funded for a limited number of laboratory studies. This project will extend those studies to: (I) find out how many myeloma patients have ‘healthier’ immune systems It is important that antibiotics are not given to patients if they do not need them. We aim to use different blood tests to find out how many myeloma patients have a damaged immune system and, as a consequence, may need antibiotics. Patients with ‘healthier’ immune systems may have a lower risk of infection and may not get any benefit from taking antibiotics. Indeed, these healthier patients may be better off receiving infection education and prompt antibiotics if a random infection occurs. In summary, this important work will help improve treatment pathways so that the decision to prescribe antibiotics can be personalised to at-risk patients, and, ultimately, this work will help reduce the over-prescription of antibiotics. (II) measure whether infections help cancer cells Because myeloma patients have a damaged immune system, infection is much more likely. The infection can then make the cancer worse by delaying cancer treatment because the patient is ill. Oddly, the immune system reaction to the infection can also help the cancer cells grow faster. Unfortunately, this remains poorly understood and we would like to find out more. This is important because it will show us whether the antibiotics can help stop the cancer growing. (III) find out more quickly how the cancer is growing To find out how infections make the cancer worse, we will need to measure the response to cancer treatment more precisely. To do this, we will use new tests that are more advanced than those used currently in clinical practice. This is important because it will enable us to more accurately assess the links between infection and cancer growth. It will also enable us to identify, more quickly, patients who are not responding to treatment. These patients may be more suited to different types of treatment. This is important because it could reduce the time patients are on the wrong treatment, and thus reduce the time they are at risk of getting an infection, whilst saving money on expensive drugs that are not working.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;5.1 PHARMACEUTICALS
Health categories: CANCER AND NEOPLASMS