A Systematic Review and Meta-Analysis to Inform Cancer Screening Guidelines in Idiopathic Inflammatory Myopathies

Alexander G. S. Oldroyd; Andrew B. Allard; Jeffrey P. Callen; Hector Chinoy; Lorinda Chung; David Fiorentino; Michael D. George; Patrick Gordon; Kate Kolstad; Drew J. B. Kurtzman; Pedro M. Machado; Neil J. McHugh; Anna Postolova; Albert Selva-O'Callaghan; Jens Schmidt; Sarah Tansley; Ruth Ann Vleugels; Victoria P. Werth; Rohit Aggarwal

Disclosures

Rheumatology. 2021;60(6):2615-2628.

Abstract and Introduction

Abstract

Objectives: To identify clinical factors associated with cancer risk in the idiopathic inflammatory myopathies (IIMs) and to systematically review the existing evidence related to cancer screening.

Methods: A systematic literature search was carried out on Medline, Embase and Scopus. Cancer risk within the IIM population (i.e. not compared with the general population) was expressed as risk ratios (RR) for binary variables and weighted mean differences (WMD) for continuous variables. Evidence relating to cancer screening practices in the IIMs were synthesized via narrative review.

Results: Sixty-nine studies were included in the meta-analysis. DM subtype (RR 2.21), older age (WMD 11.19), male sex (RR 1.53), dysphagia (RR 2.09), cutaneous ulceration (RR 2.73) and anti-transcriptional intermediary factor-1 gamma positivity (RR 4.66) were identified as being associated with significantly increased risk of cancer. PM (RR 0.49) and clinically amyopathic DM (RR 0.44) subtypes, Raynaud's phenomenon (RR 0.61), interstitial lung disease (RR 0.49), very high serum creatine kinase (WMD −1189.96) or lactate dehydrogenase (WMD −336.52) levels, and anti-Jo1 (RR 0.45) or anti-EJ (RR 0.17) positivity were identified as being associated with significantly reduced risk of cancer. Nine studies relating to IIM-specific cancer screening were included. CT scanning of the thorax, abdomen and pelvis appeared to be effective in identifying underlying asymptomatic cancers.

Conclusion: Cancer risk factors should be evaluated in patients with IIM for risk stratification. Screening evidence is limited but CT scanning could be useful. Prospective studies and consensus guidelines are needed to establish cancer screening strategies in IIM patients.

Introduction

Idiopathic inflammatory myopathies (IIMs) are chronic multisystem autoimmune conditions that may cause muscle inflammation (myositis), skin manifestations and interstitial lung disease (ILD).^[1,2] Adult-onset IIMs are associated with increased risk of cancer. A recent meta-analysis of such studies reported a standardized incidence ratio of 4.66 for DM and 1.75 for PM.^[3] The generally accepted definition of cancer associated myositis (CAM) is malignancy within 3 years of IIM onset. Cancer remains the leading cause of death for adults with IIM.^[4–6]

Around one in four patients are diagnosed with cancer within 3 years before or after IIM onset;^[4] however, risk varies according to the presence/absence of certain factors.^[7] Unfortunately, the data regarding risk factors are largely derived from retrospective studies with small populations, thus limiting the ability to form robust conclusions and extrapolate to the wider population.

Meta-analysis of existing evidence could synthesize results across studies and identify factors associated with cancer in IIM populations, thus potentially accounting for biases, such as publication bias and outlier studies. Further, assimilation of evidence relating to cancer screening specific to CAM could provide an evidence base informing clinical screening practices and facilitate the formulation of cancer screening guidelines.

The International Myositis Assessment and Clinical Studies Group (IMACS), the largest international group for scientific studies in myositis, began a special interest group to develop evidence-based cancer screening guidelines for newly diagnosed IIM patients. This meta-analysis forms an important component in guideline formation. Therefore, the overall aim of this study is to identify risk factors associated with CAM using meta-analysis, and to systematically review existing evidence relating to CAM screening approaches.

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Table 1. Meta-analysis results, including calculated effect sizes, heterogeneity and publication bias for each factor
Domain	Factor	RR/WMD (95% CI)	GRADE certainty rating^a	Heterogeneity		Egger's test, P-value
P-value	I ², %
Subtype^b	DM	2.21 (1.78, 2.77)	Moderate	0.134	24.8	0.162
	PM	0.49 (0.37, 0.65)	Moderate	0.061	34.0	0.603
	CADM	0.44 (0.20, 0.97)	Low	0.751	0.0	—
	ASS	0.28 (0.00, 6554.79)	Low	0.017	82.4	—
Demographics	Age	11.19 (9.29, 13.08)	High	0.001	56.1	0.859
Demographics	Male sex	1.53 (1.34, 1.75)	High	0.101	24.2	0.081
Clinical features	Dysphagia	2.09 (1.21, 3.60)	High	<0.0001	83.6	0.310
	Cutaneous ulceration	2.73 (1.33, 5.59)	Moderate	0.389	3.1	—
	Raynaud's phenomenon	0.61 (0.39, 0.95)	Moderate	0.698	0.0	0.055
	ILD	0.49 (0.32, 0.76)	High	0.011	52.3	0.381
Blood parameters	CK	−1189.96 (−2132.74, −247.18)	Moderate	<0.0001	84.5	0.182
	LDH	−336.52 (−514.40, −158.64)	Moderate	0.093	44.8	—
	ALT	36.29 (−313.18, 385.77)	Low	0.001	85.5	—
	ESR	4.4 (−6.2, 14.9)	Low	0.028	60.2	—
Autoantibodies	Anti-TIF1γ	4.68 (3.37, 6.48)	High	<0.0001	68.8	0.543
	Anti-NXP2	1.16 (0.73, 1.87)	Moderate	0.278	17.4	0.271
	Anti-c	1.59 (0.33, 7.74)	Low	0.217	34.5	—
	Anti-HMGCR	0.55 (0.19, 1.61)	Low	0.277	21.6	—
	Anti-MDA5	0.17 (0.02, 1.28)	Low	0.457	0.0	—
	Anti-Mi2	1.05 (0.28, 3.92)	Low	0.381	5.6	—
	Anti-SRP	0.40 (0.14, 1.21)	Low	0.790	0.0	—
	Any ASS antibody	0.41 (0.26, 0.64)	High	0.746	0.0	<0.001
	Anti-Jo1	0.45 (0.25, 0.84)	High	0.700	0.0	0.051
	Anti-PL7	0.68 (0.15, 3.07)	Low	0.541	0.0	—
	Anti-PL12	1.59 (0.89, 2.86)	Low	0.789	0.0	—
	Anti-EJ	0.17 (0.07, 0.44)	Low	0.964	0.0	—
	Anti-OJ	1.56 (0.68, 3.52)	Low	0.870	0.0	—
	Anti-KS	1.23 (0.05, 30.12)	Very low	0.717	0.0	—
	MSA negative	0.89 (0.50, 1.59)	Low	0.073	50.4	—
	ANA positivity	0.91 (0.58, 1.41)	Low	<0.0001	75.0	0.113

^aGrading of Recommendations, Assessment, Development and Evaluations certainty rating: very low (the true effect is probably markedly different from the estimated effect), low (the true effect might be markedly different from the estimated effect), moderate (the authors believe that the true effect is probably close to the estimated effect), and high (the authors have a lot of confidence that the true effect is similar to the estimated effect). ^bThe risk of cancer for each IIM subtype is estimated against each study's wider IIM population, not the general population. ALT: alanine transaminase; ASS: anti-synthetase syndrome; CADM: clinically amyopathic dermatomyositis; CI: confidence interval; CK: creatine kinase; DM: dermatomyositis; HMGCR: 3-hydroxy 3-methylutaryl coenzyme A reductase; ILD: interstitial lung disease; LDH: lactate dehydrogenase; MDA5: melanoma differentiation-associated gene 5; MSA: myositis specific autoantibody; NXP2: nuclear matrix protein 2; RR: risk ratio; SAE1: small ubiquitin-like modifier-1 activating enzyme; SRP: signal recognition particle; TIF1γ: transcriptional intermediary factor-1 gamma; WMD: weighted mean difference.

Table 2. Details of identified studies reporting utility of cancer screening investigations in IIM populations
Study	Country	Study type	IIM subtypes included	Population size	Screening modality assessed	Timing of screening	No. cancer cases identified	Control modality	No. cancer cases identified	Comments
Kidambi et al. [83]	USA	Retrospective	DM	79 in total Upper GI endoscopy 47 Lower GI endoscopy 67	Upper and lower GI endoscopy	Mean 6.8 years (S.D. 6.6) after DM onset	0 2 cases of Barrett's oesophagus 10 adenoma	NA	NA
Maliha et al. [84]	Canada	Retrospective	DM 31, PM 1, overlap 25, IBM 1, orbital 1, unspecified subtype 4	63	¹⁸F-FDG PET/CT	'Average' time of 9 months after IIM diagnosis	0	'Conventional', physical and gynaecological examination, CBC, serum biochemistry, LFTs, serum protein electrophoresis, urinalysis, CXR, gastroscopy, colonoscopy, CT of thorax, abdomen and pelvis, mammography, endovaginal USS, serum tumour markers	3 breast cancer via mammography, squamous cell carcinoma via examination, multiple myeloma via blood tests	¹⁸F-FDG PET/CT lead to more biopsies compared with conventional screening (8 vs 5)
Leatham et al. [85]	USA	Retrospective	DM	400		Median 4.2 years (IQR 1.7–8.0.6) between symptom onset and screening	29 cancers across 27 patients diagnosed after DM onset 17 cancers (16 patients) diagnosed by blind screening CT abdomen 4 Mammography 3 CBC 3 Colonoscopy 2 PSA 2 CT thorax 1 CT pelvis 1	NA	NA	2 cancers (breast cancer and DLBCL) were diagnosed via repeat 'blind screening'. Increasing age was only identified cancer risk factor.
Huang et al. [86]	China	Retrospective	DM and PM	129 PM 30, DM 99	WBMRI	Mean disease duration 30.8 months (S.D. 47.9, range 10 days to 19 years)	5, all DM 3 NPC 1 ovarian 1 thyroid cancer	NA	NA
Whitmore et al. [87]	USA	Retrospective	DM	14	Serum CA-125	Median disease duration 15.5 months (range 7–24)	4 ovarian cancer	NA	NA	Pre-diagnosis serum CA-125 levels were found to be higher in 2 (50%) of ovarian cancer cases. Normal serum CA-125 levels were seen in all controls.
Sparsa et al. [45]	France	Retrospective	DM and PM	40	History, physical and pelvic examination, CBC, ESR, general chemistry screen, LFTs, CXR, mammography, CT TAP, upper and lower GI endoscopy, 'small bowel radiologic examination', thyroid imaging, MRI, PET-CT, cancer-associated antigens, bone marrow biopsy, laparotomy	Not reported for whole cohort Screening occurred between 12 months prior to and 8 months after IIM onset in cancer cases	Total 122 investigations, 30 revealed malignancy 35 tests were 'directed', 19 (54%) were positive 87 tests were 'blind', 11 (13%) were positive	NA	NA	CT TAP revealed most 'blind' screening cancers - 5/18 (28%) were positive
Selva- O'Callaghan et al. [88]	Spain	Prospective	49 DM, 6 PM	55	¹⁸F-FDG PET/CT	Within 6 months period after IIM diagnosis	Positive in 7 cases (1 false-positive), negative in 44 cases (3 false-negatives) and inconclusive in 4 cases	CT abdomen and pelvis, mammography, gynaecologic examination, ovarian USS, tumour markers (CA-125, CA-19.9, CEA, PSA)	Positive in 9 cases (2 false-positive) 5 breast, 1 lung, 1 pancreas, 1 vagina, 1 colon Negative in 46 cases (2 false-negatives)	¹⁸F-FDG PET/CT PPV was 86%, NPV was 94%
Amoura et al. [89]	France	Retrospective	50 DM, 52 PM	102	CEA > 5 ng/ml CA15-3 > 25 units/ml CA19-9 > 37 units/ml CA125 > 35 units/ml	Not reported	CEA increased in 4 patients, no cancer diagnoses CA15-3 increased in 22, 2 cancer diagnoses CA19-9 increased in 11 patients, 3 cancer diagnoses CA125 increased in 8 patients, 5 cancer diagnoses CA19-9 and CA125 were both increased in 3 patients, all 3 were diagnosed with cancer	NA	NA
Lim et al. [40]	Taiwan	Retrospective	98 DM, 53 PM	152	CEA > 5 ng/ml CA125 > 35 units/ml CA19-9 > 34 units/ml CA15-3 > 25 units/ml AFP >12 ng/ml	Mean 6.1 years (S.D. 5.7) after IIM onset	CA15-3 increased in 9 patients, no cancer diagnoses CA125 increased in 18 patients, 1 cancer diagnosis CA19-9 increased in 10, 1 cancer diagnosis AFP increased in 4, no cancer diagnoses CEA increased in 8, 3 cancer diagnoses			8 (89%) of the 9 with elevated CA15-3 levels developed ILD

¹⁸F-FDG PET/CT: ¹⁸F-fluorodeoxyglucose PET/CT; AFP: alpha fetoprotein; CA: carbohydrate antigen; CBC: complete blood count; CEA: carcinoembryonic antigen; CT TAP: computed tomography thorax, abdomen and pelvis; CXR: chest X-ray radiograph; DLBCL: diffuse large B cell lymphoma; GI: gastrointestinal; IIM: idiopathic inflammatory myopathy; ILD: interstitial lung disease; IQR: interquartile range; LFT: liver function tests; NA: not applicable; NPC: nasopharyngeal carcinoma; NPV: negative predictive value; PPV: positive predictive value; PSA: prostate-specific antigen; USS: ultrasound scan; WBMRI: whole body magnetic resonance imaging.

Authors and Disclosures

Alexander G. S. Oldroyd^1–4, Andrew B. Allard⁵, Jeffrey P. Callen⁶, Hector Chinoy ^1,2,4, Lorinda Chung^7,8, David Fiorentino⁹, Michael D. George^10,11, Patrick Gordon¹², Kate Kolstad⁹, Drew J. B. Kurtzman¹³, Pedro M. Machado ^14–16, Neil J. McHugh ¹⁷, Anna Postolova¹⁸, Albert Selva-O'Callaghan¹⁹, Jens Schmidt²⁰, Sarah Tansley ^5,20, Ruth Ann Vleugels^21,22, Victoria P. Werth^23,24 and Rohit Aggarwal ²⁵

¹National Institute for Health Research Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, ²Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, ³Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, ⁴Department of Rheumatology, Salford Royal NHS Foundation Trust, Salford, ⁵Royal National Hospital for Rheumatic Diseases, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK, ⁶Division of Medicine, Department of Medicine, University of Louisville, Louisville, KY, ⁷Division of Immunology and Rheumatology, Department of Medicine and Dermatology, Stanford University, Stanford, ⁸Palo Alto Health Care System, Palo Alto, ⁹Department of Dermatology, School of Medicine, Stanford University, Stanford, CA, ¹⁰Division of Rheumatology, ¹¹Division of Epidemiology, University of Pennsylvania, Philadelphia, PA, USA, ¹²Department of Rheumatology, King's College Hospital NHS Foundation Trust, London, UK, ¹³Department of Dermatology, Wright State University, Dayton, OH, USA, ¹⁴Centre for Rheumatology and Department of Neuromuscular Diseases, University College London, ¹⁵National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London Hospitals NHS Trust, ¹⁶Department of Rheumatology, Northwick Park Hospital, London North West University Healthcare NHS Trust, London, ¹⁷Department of Pharmacy and Pharmacology, University of Bath, Bath, UK, ¹⁸Department of Immunology and Rheumatology, Stanford Health Care, Stanford, CA, USA, ¹⁹Systemic Autoimmune Unity, Vall D'Hebron General Hospital, Universitat Autonoma de Barcelona, Barcelona, Spain, ²⁰Department of Neurology, Neuromuscular Centre, Göttingen, Germany, ²¹Harvard Medical School, ²²Department of Dermatology, Brigham and Women's Hospital, Boston, MA, ²³Department of Dermatology, University of Pennsylvania, ²⁴Division of Dermatology, Corporal Michael J. Crescenz VA Medical Center, Philadelphia and ²⁵Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

Correspondence to
Rohit Aggarwal, Division of Rheumatology and Clinical Immunology, Arthritis and Autoimmunity Center (Falk), UPMC Myositis Center, University of Pittsburgh, 3500 Terrace Street, BST S 700, Pittsburgh, PA, USA. E-mail: aggarwalr@upmc.edu

Disclosure statement
R.A. served as a consultant for Kezar, Csl Behring, AstraZeneca, Octapharma, BMS, Pfizer, Janssen, Mallinckrodt, Alexion, Q32, Argenx, Boehringer-Ingelheim, Corbus and EMD-Serono; and received research funding from Pfizer, BMS, Genentech, Kezar, Csl Behring and Mallinckrodt. L.C. served on an advisory board and received grant funding from Boerhinger Ingelheim; served on an advisory board for Eicos Sciences; served on an advisory board for Bristol-Myers Squibb; received consulting fees for Mitsubishi Tanabe; and serves on a data safety monitoring board for Reata. M.D.G. received a research grant from Bristol-Myers Squibb for unrelated work; and consulting fees from Dysimmune Diseases Foundation. P.M.M. received consulting/speaker's fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, outside the submitted work. R.A.V. received a research grant from Pfizer. H.C. received research grants, travel grants, consultancy or speaker honoraria from AbbVie, Amgen, BMS, Biogen, Janssen, Lilly, Novartis and UCB. V.P.W. served as a consultant for Kezar, CSL Behring, AstraZeneca, Octapharma, Pfizer, Janssen, Neovacs and Idera; and received research funding from Pfizer, CSL Behring and Corbus.

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A Systematic Review and Meta-Analysis to Inform Cancer Screening Guidelines in Idiopathic Inflammatory Myopathies

Abstract and Introduction

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