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Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with immunotherapy
JGO-00836; No. of pages: 5; 4C: Journal of Geriatric Oncology xxx (2019) xxx
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Journal of Geriatric Oncology
Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with immunotherapy Andrea Sbrana b,1, Rachele Antognoli a,1, Giuseppe Pasqualetti a,⁎, Giuseppe Linsalata a, Chukwma Okoye a, Valeria Calsolaro a, Federico Paolieri b, Francesco Bloise b, Sergio Ricci b, Andrea Antonuzzo b,⁎⁎, Fabio Monzani a a b
Geriatrics Unit, Department of Clinical & Experimental Medicine, University of Pisa, via Savi 10, 56127 Pisa, Italy Oncology Unit 1, Pisa University Hospital, via Roma 67, 56126 Pisa, Italy
a r t i c l e
i n f o
Article history: Received 19 April 2019 Received in revised form 7 September 2019 Accepted 30 September 2019 Available online xxxx Keywords: Older patients Advanced cancer Immunotherapy Frailty MPI score
a b s t r a c t Background: Older adults with cancer are less likely to be offered treatment for cost-benefit concern. The MultiPrognostic Index (MPI) has been validated in various clinical settings for survival estimation. We aimed to evaluate MPI as a screening tool for older adults with cancer eligible to receive immunotherapy. Patients and Methods: Older adults with advanced or metastatic cancer, admitted to the Oncology Day Hospital of the University Hospital of Pisa from January 2017 to May 2018, eligible to receive immunotherapy were prospectively enrolled. In addition to routine oncological evaluation, each patient received a comprehensive geriatric assessment with MPI calculation. Overall survival (Cox-adjusted curve) was stratified by tertiles of MPI score. Drug toxicity was assessed according to the National Cancer Institute Common Terminology Criteria for Adverse Events (Version 4.03: June 14, 2010). Results: Seventy-nine patients [26.6% women, mean age (±SD) 74.0 ± 6.1 years] were enrolled with the following diagnosis: melanoma (51.9%), non-small cell lung cancer (25.3%), renal cell cancer (12.7%), urothelial cancer (8.9%) and Merkel cell carcinoma (1.2%). Median follow-up was 7 months (range 1–35). The patients' survival rate resulted progressively longer proceeding from the first to the third MPI tertile [HR 1.76 (0.49–6.31) Vs 2nd tertile, p b 0.05; HR 5.33 (1.68–16.89) Vs 3rd tertile, p b 0.01]. Conclusions: MPI score is an effective tool for the stratification of older patients with cancer eligible for immunotherapy with checkpoint inhibitors. Further studies are required to achieve conclusive remarks on MPI usefulness in different underlying tumor types. © 2019 Published by Elsevier Ltd.
1. Introduction Despite the increasing incidence and prevalence of cancer among older population, patients older than 75 years account for only 10% of those enrolled in clinical trials [1]. Older adults with cancer are less likely to be offered treatment because of several reasons including perceived and/or objective less resilience to tolerate certain therapies [2]. To make personalized treatment decisions and to anticipate serious adverse effects, it is important to identify those older adults who are at risk. Immunotherapy has become a cornerstone for the treatment of several types of tumors given its tolerability and efficacy in various clinical settings [3]. Nonetheless, to date, immunotherapy studies have included only a minority of older adults with little information on the
treatment efficacy and safety profile. Conversely, in everyday clinical practice, adults older than 65 years represent an important portion of immunotherapy candidates thus, more information on this setting is needed [4,5]. In general, older adults with cancer need an appropriate preliminary evaluation before treatment, to avoid under- and over-treatment [6]. The efficacy of oncologic treatment can be affected by several limiting factors, e.g. comorbidity, polypharmacy and drug interactions, cognitive and functional status, as well as social environment [7]. For these reasons, several geriatric assessment tools for the stratification of cancer treatment have been proposed [8]. However, there is no consensus on the most useful tool for predicting prognosis and toxicity risk in older adults with cancer [9,10].
⁎ Corresponding author at: Geriatrics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Via Savi 10, 56126 Pisa, Italy. ⁎⁎ Corresponding author. E-mail addresses: [email protected] (G. Pasqualetti), [email protected] (A. Antonuzzo). 1 SA and RA equally contributed to the manuscript as first authors
https://doi.org/10.1016/j.jgo.2019.09.010 1879-4068/© 2019 Published by Elsevier Ltd.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010
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The Multi-Prognostic Index (MPI) is an effective tool, derived from a standard comprehensive geriatric assessment (CGA), validated in many clinical settings to stratify prognosis of older adults with cancer [11,12]. MPI, which includes 63 items distributed in 8 domains, emerged as a highly accurate and well-calibrated tool for prediction of one-year mortality in older adults with cancer, enhancing clinical decision making [13]. However, so far, no prospective studies have been carried out to evaluate the usefulness of MPI to better stratify older adults with cancer eligible for immunotherapy. Thus, the current study aims to test MPI as a screening tool to enhance clinical decision making in older adults with cancer who are candidates for immunotherapy. 2. Patients and Methods The population enrolled in the study was composed of older adults with cancer consecutively attending the Oncology Day Hospital of the University Hospital of Pisa, from January 2017 to May 2018. Patients were candidate to receive immunotherapy due to advanced or metastatic cancer. The study protocol was approved by the Ethic Committee of the University Hospital of Pisa, Italy. All the patients were assessed by the Eastern Cooperative Oncology Group Performance Status (ECOG PS) scale, to establish their eligibility for receiving an oncologic active treatment [14]. Demographic, oncologic, and geriatric data were also collected and registered. Specifically, oncologic data included information about the primary site of the tumor, the sites of metastases, the type of immunotherapeutic agent, the line treatment, and the treatments undertaken prior to immunotherapy. Comprehensive geriatric assessment included the following scales: Short Physical Performance Battery (SPPB), Activity of Daily Living (ADL), Instrumental Activity of Daily Living (IADL), Short Portable Mental Status Questionnaire (SPMSQ), Mini-Nutritional Assessment (MNA), Cumulative Illness Rating Scale (CIRS) and Exton-Smith Scale (ESS) [14]. Polypharmacy and social support network were also recorded. The SPPB measures balance, gait speed and lower limb strength and endurance. Among non-disabled older adults living in the community, the SPPB was highly predictive of subsequent disability and, several studies have evaluated outcomes using the SPPB as a risk assessment of frailty in older adults [15]. We classified patients as follows [16]: frail (SPPB score of 1–4), prefrail (SPPB score of 5–8) and fit (SPPB score of 9–12). In addition to SPPB, the MPI score, an accurate index of frailty as well as a validated marker of short- and long-term mortality, was calculated incorporating the above geriatric measures as well as polypharmacy and living arrangement [2–18]. A detailed description of MPI calculation is available in Supplementary Material. Efficacy of immunotherapy as well as safety endpoints; namely, overall survival, progression-free survival, and treatment-related toxicities, with a particular attention to NCI-CTCAE (National Cancer Institute Common Terminology Criteria for Adverse Events, Version 4.03: June 14, 2010) grade 3 and 4 toxicities were analyzed and recorded throughout follow-up. 2.1. Statistical Analysis Statistical analysis was performed by using SPSS 21.0 statistical software package (SPSS Inc., Chicago, IL). Continuous variables are expressed as mean ± SD, ordinal variable as median and range and categorical variables as percentage. Descriptive statistics of the entire cohort was firstly performed. Independent sample t-test was used for normally distributed data and, Mann-Whitney U Test for non-parametric data. Univariate Cox regression analysis, adjusted for covariates resulting significantly different at baseline on the comparison among groups, was performed to identify factors significantly associated with overall survival. Then, Coxproportional regression model was applied for survival analysis. Statistical significance was given for p b 0.05.
3. Results We enrolled 79 older adults with cancer [73.4% men, mean age (± SD) 74.2 ± 6.1 years] with the following diagnosis: melanoma (51.6%), non-small cell lung cancer (NSCLC) (25.3%), renal cell cancer (12.7%), urothelial cancer (8.9%) and Merkel cell carcinoma (1.2%). According to ECOG PS, most patients had a good performance status, either 0 (40.5%) or 1 (48.1%) while, 9 patients (11.4%) had a poor performance status (2 or 3) (Table 1). Patients received immunotherapy as first- (45.6%) or second-line therapy (45.6%); only few subjects received the treatment as third- or further-line therapy (8.8%). Patients mainly received anti-PD1 (n = 67, 84.8%) and anti-PDL1 antibodies (n = 8, 10.1%), whilst a minority received ipilimumab, an anti-CTLA4 antibody (n = 4, 5.1%). Median follow-up was 7 months (range 1–35). Data on CGA are reported in Table 2. At SPPB examination only 36.7% of the patients were classified as fit (score 9–12) while, 46.8% as pre-frail (score 5–8) and 16.5% as frail (score 1–4). Also, by SPPB score stratification no significant differences were obtained in terms of overall survival (data not shown). Conversely, patients showed a good clinical status in most of the other CGA scales: 68.4% with 5–6 ADL activities,76.0% with 0–2 errors at SPMSQ, 82.3% without bedsore risk (ESS score 16–20) and 40.5% with normal nutritional state (MNA score 24–30). By Cox regression analysis, patients with an elevated burden of comorbidities (CIRS-C ≥ 3) showed a significantly higher risk of mortality [HR 2.32 (95% C.I., 1.09–4.94), p b 0.05] while, a good nutritional state (MNA ≥ 24) significantly reduced it [HR 0.36 (95% C.I., 0.02–0.57), p b 0.05]. No significant correlation was obtained with the other CGA items (data not shown). In order to increase the overall prognostic value of CGA, the MPI score was then calculated. Table 3 summarizes the oncologic and geriatric evaluation of patients clustered by MPI tertiles. In detail, apart from the expected variable stratification of each CGA item, significant differences in terms of ECOG PS and treatment lines were obtained (p b 0.05, for both). Moreover, patients' overall survival resulted significantly different according to MPI tertiles (Fig. 1). Specifically, patients in the first MPI tertile showed 76% increased survival rate than those in the 2nd tertile [HR 1.76 (95% C.I., 0.49–6.31), p b 0.05], which became 5 time longer respect to those in the 3rd tertile [HR 5.33 (95% C.I., 1.68–16.89), p b 0.01].
Table 1 Baseline clinical characteristics of the whole cohort of patients (n=79). Age (years) Women Primary tumor site • Melanoma • Non-Small Cell Lung Cancer (NSCLC) • Renal cell cancer • Urothelial cancer • Merkel cell carcinoma Setting at the moment of diagnosis • Local • Locally advanced • Metastatic Line of therapy • First • Second • Third or more Type of drug • Anti-PD1 antibodies (pembrolizumab, nivolumab) • Anti-PDL1 antibodies (avelumab, durvalumab) • Anti-CTLA4 antibodies ECOG PS • 0 • 1 • 2–3
74.2 ± 6.1 21 (26.6%) 41 (51.9%) 20 (25.3%) 10 (12.7%) 7 (8.9%) 1 (1.2%) 23 (29.1%) 27 (34.2%) 29 (36.7%) 36 (45.6%) 36 (45.6%) 7 (8.8%) 67 (84.8%) 8 (10.1%) 4 (5.1%) 32 (40.5%) 38 (48.1%) 9 (11.4%)
ECOG PS: Eastern Cooperative Oncology Group Performance Status. All the data are reported as mean ± SD or number (%), as appropriate.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010
A. Sbrana et al. / Journal of Geriatric Oncology xxx (2019) xxx Table 2 Baseline comprehensive geriatric assessment of the whole cohort of patients (n=79). ADL • 5–6 • 2–4 • 0–1 IADL • 6–8 • 3–5 • 0–2 SPMSQ • 0–2 • 3–4 • 5–7 • 8–10 MNA • 24–30 • 18–23.5 • 0–17 CIRS-C CIRS-S Number of drugs • 0 • 1–3 • 4–6 • 7 or more ESS • 0–5 • 6–10 • 11–15 • 16–20 SPPB • 1–4 (frail) • 5–8 (pre-frail) • 9–12 (fit) Living arrangements • Alone • With their family • Dedicated health structure MPI
54 (68.4%) 20 (25.3%) 5 (6.3%) 16 (20.3%) 42 (53.1%) 21 (26.6%) 60 (76%) 10 (12.6%) 6 (7.6%) 3 (3.8%) 32 (40.5%) 37 (46.8%) 10 (12.7%) 2 (1–6) 1.46 (0.46–2.23) 9 (11.4%) 43 (54.4%) 16 (20.3%) 11 (13.9%) 1 (1.2%) 3 (3.8%) 10 (12.7%) 65 (82.3%)
Thus, the prognostic evaluation of life expectancy is a corner stone in the pretreatment clinical classification of older patients with cancer eligible for immunotherapy. In this setting, there is growing need to select patients in order to treat those who might really receive clinical benefit, avoiding useless treatments as well as serious drug-related adverse events. Phase 3 trials that led to the Drug Regulatory Agencies' approval of checkpoint inhibitors included only a small number of older patients, making the choice of treatment somewhat arbitrary in older patients [22]. Thus, real-life data with prognostic evaluation of older adults with cancer are needed to better balance harm–and cost–benefits of immunotherapy. Over the last years, some generic prognostic scores for the risk stratification of older patients with cancer have been proposed but, there is no consensus on the most appropriate tool for predicting prognosis and toxicity risk [9,23,24]. MPI was previously validated in older hospitalized patients, suffering from major diseases, including several types of cancer [25,26], with a significant higher predictive power than other widely used frailty indexes [10–13,15–18]. In a prospective clinical trial, enrolling older adults with locally advanced or metastatic solid cancers, MPI outperformed multidimensional geriatric assessment in predicting mortality [27]. Moreover, in a large cohort of older patients with cancer, a modified multidimensional prognostic index (Onco-MPI) for mortality prediction has been developed [13]. Different from the classical Table 3 Patients' clinical characteristics, overall survival and drugs' toxicity according to MPI tertiles.
13 (16.5%) 37 (46.8%) 29 (36.7%) 17 (21,5%) 58 (73.4%) 4 (5.1%) 0.25 (0.06–0.75)
ADL: Activities of Daily Living; IADL Instrumental Activities of Daily Living; SPMSQ: Short Portable Mental Status Questionnaire; MNA: Mini-Nutritional Assessment; CIRS-C: Cumulative Illness Rating Scale-Comorbidity; CIRS-S: Cumulative Illness Rating Scale-Severity; ESS: Exton-Smith Scale; SPPB: Short Physical Performance Battery; MPI: Multi-Prognostic Index. Data are reported as number (%) or median (range) as appropriated.
Forty-three patients (54.5%) experienced at least one toxicity from the treatment, although only 6 (7.6%) suffered from severe toxicity (i.e. grade 3 or 4 according to NCI-CTCAE criteria). In detail, patients mainly experienced dermatologic adverse events [13 patients, (16.5%), mostly skin rashes and pruritus], diarrhea [12 patients (15.2%)] and endocrinological toxicity [10 patients (12.7%), mostly hypothyroidism]. 4. Discussion The current study shows that MPI is a useful tool for prognostic stratification of older patients with either local or advanced cancer, treated with immunotherapy. Indeed, patients with the highest MPI score experienced the worst overall survival, with a 5-fold increased risk of mortality. According to literature [19], among the CGA domains by which MPI derives, only nutritional state and the burden of comorbidities emerged as independent risk factors of overall survival. Partially at odds with prior studies, no significant association was also observed between polypharmacy and overall survival [20]. However, previous reported data are limited by study confounders, inconsistent definitions for polypharmacy, heterogeneous cancer types and stages, and the complex relationship between medication regimens and outcomes. Finally, in our cohort the safety profile of immunotherapy does not differ from that observed in younger patients and phase 3 clinical trials [3,7,21]. Several studies highlighted immunotherapy as a valuable option for the treatment of adults affected by several types of cancer (melanoma, NSCLC, and genitourinary cancers) [21]. Immunotherapy is not exempt from toxicities, especially in case of high burden of comorbidity.
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First tertile (0.06–0.30)
Second tertile (0.31–0.52)
Third tertile (0.53–0.75)
28 74.2 ± 6.7 9 (32.1%)
32 75.8 ± 4.6 5 (15.6%)
19 74.9 ± 5.8 7 (36.8%)
17 (60.7%) 5 (17.9%) 4 (14.3%) 2 (7.1%) 0
13 (40.6%) 12 (37.5%) 5 (15.7%) 1 (3.1%) 1 (3.1%)
11 (57.9%) 3 (15.8%) 1 (5.2%) 4 (21.1%) 0
Setting at the moment of diagnosis • Local 10 (35.7%) • Locally Advanced 10 (35.7%) • Metastatic 8 (28.6%)
9 (28.1%) 7 (21.9%) 16(50%)
4 (21.1%) 10 (52.6%) 5 (26.3%)
Line of therapy⁎ • First • Second • Third or more
15 (53.6%) 10 (35.7%) 3 (10.7%)
12 (37.5%) 19 (59.4%) 1 (3.1%)
9 (47.4%) 7 (36.8%) 3 (15.8%)
17 (60.7%) 11 (39.3%) 0 5 (5–6) 5 (3–8) 0 (0–5)
7 (21.9%) 23 (71.9%) 2 (6.2%) 5 (3–6) 3 (1–5) 0 (0–4)
8 (42.1%) 4 (21.1%) 7 (36.8%) 2 (0–4) 1 (0–3) 5 (0−10)
21 (75%) 7 (25%) 0% 2 (1–4) 1.4 ± 0.3 20 (15–20) 28.8 ± 2.9 18 (66.7%) 3 (10.7%)
8 (25%) 21 (65.6%) 3 (9.4%) 2.5 (2–6) 1.5 ± 0.2 20 (13−20) 23 ± 3.1 15 (46.9%) 0
3 (15.8%) 9 (47.4%) 7 (36.8%) 3 (1–5) 1.7 ± 0.3 13(5–20) 13.7 ± 2.8 10 (52.6%) 3 (15.8%)
Total number of patients Age Female Primary tumor • Melanoma • NSCLC • Kidney • Urothelial • Merkel
ECOG PS⁎ • 0 • 1 • 2–3 ADL IADL SPMSQ MNA • 30-24 • 23.5-18 • 17-0 CIRS-C CIRS-S ESS Overall survival (months) Overall toxicities G3-G4 toxicities
NSCLC: Non-Small Cell Lung Cancer; ECOG PS: Eastern Cooperative Oncology Group Performance Status; ADL: Activities of Daily Living; IADL Instrumental Activities of Daily Living; SPMSQ: Short Portable Mental Status Questionnaire; MNA: Mini-Nutritional Assessment; CIRS-C: Cumulative Illness Rating Scale-Comorbidity; CIRS-S: Cumulative Illness Rating Scale-Severity ESS: Exton-Smith Scale; MPI: Multi-Prognostic Index; G3-G4: Grade 3 – Grade 4. Data are reported as mean ± SD, median (range) or number (%) as appropriate. ⁎ p b 0.05.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010
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1.0 MPI 1 MPI 2
0.8
Cum Survival
MPI 3
0.6
0.4
0.2
0.0 0
5
10
15
20
25
30
Time (Months) MPI 1 MPI 2 MPI 3
0 28 32 19
5 20 (2) 18 (3) 13 (4)
10 13 (3) 10 (4) 4 (7)
15 7 (4) 7 (5) 2 (9)
20 5 (4) 2 (6) 2 (10)
25 4 (4) 1 (6) 1 (10)
30 1 (4) 1 (6) 0 (11)
Number at Risk (cumulative number of deaths) during follow-up Fig. 1. Cox-adjusted overall survival curve according to MPI (Multi-Prognostic Index) tertiles.
MPI, it included the following items to evaluate functional, cognitive and nutritional status: ECOG PS (instead of ESS), MMSE (instead of SPMSQ) and BMI (instead of MNA), respectively. Interestingly, OncoMPI appeared a highly accurate and well-calibrated predictive tool for one-year mortality and, as such, it might be useful for clinical decision making in older adults with cancer [13]. Nonetheless, older patients enrolled in that study were not selected on the basis of specific cancer treatments and, no data were reported regarding immunotherapy [13]. The main strength of the current study relies both on the age of enrolled patients (averaging 74 years), which is uncommon in clinical trials, and on the fact that, for the first time, MPI has been utilized for clinical stratification of older adults with advanced/metastatic cancer, eligible for immunotherapy. It is worth noting that our cohort was characterized by the presence of a small proportion of patients with poor performance status by ECOG PS (11.4% with score 2–3) in the face of a high prevalence of frail or pre-frail patients by SPPB (63.3%), malnourished or at risk of malnutrition by MNA (59.5%). Nonetheless, the present study has some limitations. Firstly, the chosen cancer population displayed heterogeneity in terms of primary tumor and different line of treatments. Moreover, the small number of adults with NSCLC may represent a potential weakness of the study, although they account for almost one fourth of the whole cohort. The relative over-representation of melanoma is due to the difference of immunotherapy indication in NSCLC and melanoma in Italy in the observation period. Indeed, adults with melanoma could receive immunotherapy both in first and second line for metastatic disease, whereas adults with NSCLC could receive it only in second line for metastatic disease. By MPI stratification, older adults with NSCLC were more frequently allocated in the second MPI tertile, also because of the high burden of comorbidity (mean CIRS-C 4.2 Vs 1.9 in NSCLC patients of the 1st tertile). However, older adults with melanoma or NSCLC exhibited similar
survival trends, although without statistical significance in the latter group of patients. Overall, our data do not allow final conclusions regarding potential heterogeneous usefulness of MPI stratification in different cancer types. Given these premises, differences in tumor diagnosis based on MPI or CGA scales, did not interfere with the overall survival analysis and, routine use of MPI stratification may represent a reliable and effective tool for selecting older adults with cancer receiving immunotherapy. Indeed, older adults are often affected by a variable degree of comorbidity and disability, which represents a crucial clinical challenge for oncologists. In a multidisciplinary team, patients that are found to be at risk in a pre-screening test (e.g. G8 questionnaire [25]) might be submitted to comprehensive geriatric assessment with calculation of the MPI score. Thus, MPI score allows to stratify older adults in relation to both frail degree and survival expectancy, making it possible to exclude from treatment frail patients with reduced life expectancy (in our series MPI N 0.50). Patients with intermediate MPI score (between 0.31 and 0.50, in our cohort) could be more closely monitored, with a case-by-case tailored therapeutic decision. It might be interesting to explore in future studies conducted in patients with higher MPI score whether an early geriatric assessment with clinical interventions (directed to patients' needs) may yield to a better survival, thus allowing the use of immunotherapy even in this setting. In conclusion, MPI score is an effective screen for treatment stratification of older patients eligible for checkpoint inhibitors, encouraging its routinely use in everyday clinical practice. Our findings support the use of immunotherapy in older patients with advanced cancer, provided that baseline comprehensive geriatric assessment is performed. However, further studies with larger populations are needed in order to achieve a conclusive remark on the efficacy of MPI in diverse underlying tumor types.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010
A. Sbrana et al. / Journal of Geriatric Oncology xxx (2019) xxx
Author Contribution Statement Pasqualetti G, Sbrana A, Linsalata G, Antognoli R, Paolieri F, Bloise F, Okoye C and Calsolaro V equally contributed to study conduction and data collection. Pasqualetti G, Sbrana A, Linsalata G, Antognoli R and Monzani F participated to data analysis and manuscript writing. Ricci S, Antonuzzo A and Monzani F designed the study and participated to interpretation of results. Monzani F, Sbrana A and Antognoli R revised the manuscript. Declaration of Competing Interest Nothing to declare. Acknowledgements Nothing to declare. References [1] Hurria A, Dale W, Mooney M, Rowland JH, Ballman KV, Cohen HJ, et al. Designing therapeutic clinical trials for older and frail adults with cancer: U13 conference recommendations. J Clin Oncol 2014;32:2587–94. https://doi.org/10.1200/JCO.2013.55. 0418. [2] Hurria A, Mohile S, Gajra A, Klepin H, Muss H, Chapman A, et al. Validation of a prediction tool for chemotherapy toxicity in older adults with cancer. J Clin Oncol 2016; 34:2366–71. https://doi.org/10.1200/JCO.2015.65.432. [3] Nishijima TF, Shachar SS, Nyrop KA, Muss HB. Safety and tolerability o PD-1/PD-L1 inhibitors compared with chemotherapy in patients with advanced cancer: a meta-analysis. Oncologist 2017;22:470–9. https://doi.org/10.1634/theoncologist. 2016-0419. [4] Poropatich K, Fontanarosa J, Samant S, Sosman JA, Zhang B. Cancer immunotherapies: are they as effective in the elderly? Drugs Aging 2017;34:567–81. https:// doi.org/10.1007/s40266-017-0479-1. [5] American Society of Clinical Oncology Educational Book vol. 38 400–414, [10.1200/ EDBK_201435 [accessed 14 April 2019]]. [6] Le Saux O, Falandry C, Gan HK, You B, Freyer G, Péron J. Inclusion of elderly patients in oncology clinical trials. Ann Oncol 2016;27:1799–804. https://doi.org/10.1093/ annonc/mdw259. [7] Muchnik E, Loh KP, Strawderman M, Magnuson A, Mohile SG, Estrah V, et al. Immune checkpoint inhibitors in real-world treatment of older adults with nonsmall cell lung cancer. J Am Geriatr Soc 2019;67(5):905–12. https://doi.org/10. 1111/jsg.15750. [8] Pamoukdjian F, Liuu E, Caillet P, Gisselbrecht M, Herbaud S, Boudou-Rouquette P, et al. Geriatric assessment and prognostic scores in older cancer patient: additional support to the therapeutic decision? Bull Cancer 2017;104:946–55. https://doi.org/ 10.1016/j.bulcan.2017.10.004. [9] Pamoukdjian F, Liuu E, Caillet P, Herbaud S, Gisselbrecht M, Poisson J, et al. How to optimize cancer treatment in older patients: an overview of available geriatric tools. Am J Clin Oncol 2019;42:109–16. https://doi.org/10.1097/COC. 0000000000000488. [10] Dent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: a review. Eur J Intern Med 2016;31:3–10.
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[11] Pilotto A, Ferrucci L, Franceschi M, D’Ambrosio LP, Scarcelli C, Cascavilla L, et al. Development and validation of a multidimensional prognostic index for one-year mortality from comprehensive geriatric assessment in hospitalized older patients. Rejuvenation Res 2008;11:151–61. https://doi.org/10.1089/rej.2007.0569. [12] Giantin V, Falci C, De Luca E, Valentini E, Iasevoli M, Siviero P, et al. Performance of the multidimensional geriatric assessment and multidimensional prognostic index in predicting negative outcomes in older adults with cancer. Eur J Cancer Care (Engl) 2018 Jan;27(1). https://doi.org/10.1111/ecc.12585. [13] Brunello A, Fontana A, Zafferri V, Panza F, Fiduccia P, Basso U, et al. Development of an oncological-multidimensional prognostic index (Onco-MPI) for mortality prediction in older cancer patients. J Cancer Res Clin Oncol 2016;142:1069–77. https://doi. org/10.1007/s00432-015-2088-x. [14] Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, et al. Toxicity and response criteria of the eastern cooperative oncology group. Am J Clin Oncol 1982;5:649–55. [15] Gary R. Evaluation of frailty in older adults with cardiovascular disease: incorporating physical performance measures. J Cardiovasc Nurs 2012;27(2):120–231. https:// doi.org/10.1097/JCN.0b013e318239f4a4. [16] Chiarantini D, Volpato S, Sioulis F, Bartalucci F, et al. Lower extremity performance measures predict long-term prognosis in older patients hospitalized for heart failure. J Card Fail 2010;16(5):390–5. https://doi.org/10.1016/j.cardfail.2010.01.004 [Epub 2010 Mar 3]. [17] Elsawy B, Higgins KE. The geriatric assessment. Am Fam Physician 2011;83(1): 48–56 Jan1. [18] Pilotto A, Addante F, Ferrucci L, Leandro G, D’Onofrio G, Corritore M, et al. The multidimensional prognostic index predicts short- and long-term mortality in hospitalized geriatric patients with pneumonia. J Gerontol A Biol Sci Med Sci 2009;64:880–7. https://doi.org/10.1093/gerona/glp031. [19] Martucci RB, Barbosa MV, D’Almeida CA, Rodrigues VD, Bergmann A, de Pinho NB, et al. Undernutrition as independent predictor of early mortality in elderly cancer patients. Nutrition 2017;34:65–70. https://doi.org/10.1016/j.nut.2016.09.011. [20] Nightingale G, Skonecki E, Boparai MK. The impact of polypharmacy on patient outcomes in older adults with cancer. Cancer J 2017;23:211–8. [21] Dine J, Gordon R, Shames Y, Kasler MK, Barton-Burke M. Immune checkpoint inhibitors: an innovation in immunotherapy for the treatment and management of patients with cancer. Asia Pac J Oncol Nurs 2017;4:127–35. https://doi.org/10.4103/ apjon.apjon_4_17. [22] Marrone KA, Forde PM. Cancer immunotherapy in older patients. Cancer J 2017;23: 219–22. https://doi.org/10.1097/PPO.0000000000000268. [23] Pilotto S, Sperduti I, Novello S, Peretti U, Milella M, Facciolo F, et al. Risk stratification model for resected squamous-cell lung cancer patients according to clinical and pathological factors. J Thorac Oncol 2015;10:1341–8. https://doi.org/10.1097/JTO. 0000000000000628. [24] Baijal P, Periyakoil V. Understanding frailty in cancer patients. Cancer J 2014;20: 358–66. https://doi.org/10.1097/PPO.0000000000000068. [25] Pilotto A, Rengo F, Marchionni N, Sancarlo D, Fontana A, Panza F, et al. Comparing the prognostic accuracy for all-cause mortality of frailty instruments: a multicentre 1-year follow-up in hospitalized older patients. PLoS One 2012;7:e29090. https:// doi.org/10.1371/journal.pone.0029090. [26] Giantin V, Valentini E, Iasevoli M, Falci C, Siviero P, De Luca E, et al. Does the multidimensional prognostic index (MPI), based on a comprehensive geriatric assessment (CGA), predict mortality in cancer patients? results of a prospective observational trial. J Geriatr Oncol 2013;4:208–17. https://doi.org/10.1016/j.jgo. 2013.04.008. [27] Bellera CA, Rainfray M, Mathoulin-Pélissier S, Mertens C, Delva F, Fonck M, et al. Screening older cancer patients: first evaluation of the G-8geriatric screening tool. Ann Oncol 2012;23:2166–72. https://doi.org/10.1093/annonc/mdr587.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010
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Journal of Geriatric Oncology
Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with immunotherapy Andrea Sbrana b,1, Rachele Antognoli a,1, Giuseppe Pasqualetti a,⁎, Giuseppe Linsalata a, Chukwma Okoye a, Valeria Calsolaro a, Federico Paolieri b, Francesco Bloise b, Sergio Ricci b, Andrea Antonuzzo b,⁎⁎, Fabio Monzani a a b
Geriatrics Unit, Department of Clinical & Experimental Medicine, University of Pisa, via Savi 10, 56127 Pisa, Italy Oncology Unit 1, Pisa University Hospital, via Roma 67, 56126 Pisa, Italy
a r t i c l e
i n f o
Article history: Received 19 April 2019 Received in revised form 7 September 2019 Accepted 30 September 2019 Available online xxxx Keywords: Older patients Advanced cancer Immunotherapy Frailty MPI score
a b s t r a c t Background: Older adults with cancer are less likely to be offered treatment for cost-benefit concern. The MultiPrognostic Index (MPI) has been validated in various clinical settings for survival estimation. We aimed to evaluate MPI as a screening tool for older adults with cancer eligible to receive immunotherapy. Patients and Methods: Older adults with advanced or metastatic cancer, admitted to the Oncology Day Hospital of the University Hospital of Pisa from January 2017 to May 2018, eligible to receive immunotherapy were prospectively enrolled. In addition to routine oncological evaluation, each patient received a comprehensive geriatric assessment with MPI calculation. Overall survival (Cox-adjusted curve) was stratified by tertiles of MPI score. Drug toxicity was assessed according to the National Cancer Institute Common Terminology Criteria for Adverse Events (Version 4.03: June 14, 2010). Results: Seventy-nine patients [26.6% women, mean age (±SD) 74.0 ± 6.1 years] were enrolled with the following diagnosis: melanoma (51.9%), non-small cell lung cancer (25.3%), renal cell cancer (12.7%), urothelial cancer (8.9%) and Merkel cell carcinoma (1.2%). Median follow-up was 7 months (range 1–35). The patients' survival rate resulted progressively longer proceeding from the first to the third MPI tertile [HR 1.76 (0.49–6.31) Vs 2nd tertile, p b 0.05; HR 5.33 (1.68–16.89) Vs 3rd tertile, p b 0.01]. Conclusions: MPI score is an effective tool for the stratification of older patients with cancer eligible for immunotherapy with checkpoint inhibitors. Further studies are required to achieve conclusive remarks on MPI usefulness in different underlying tumor types. © 2019 Published by Elsevier Ltd.
1. Introduction Despite the increasing incidence and prevalence of cancer among older population, patients older than 75 years account for only 10% of those enrolled in clinical trials [1]. Older adults with cancer are less likely to be offered treatment because of several reasons including perceived and/or objective less resilience to tolerate certain therapies [2]. To make personalized treatment decisions and to anticipate serious adverse effects, it is important to identify those older adults who are at risk. Immunotherapy has become a cornerstone for the treatment of several types of tumors given its tolerability and efficacy in various clinical settings [3]. Nonetheless, to date, immunotherapy studies have included only a minority of older adults with little information on the
treatment efficacy and safety profile. Conversely, in everyday clinical practice, adults older than 65 years represent an important portion of immunotherapy candidates thus, more information on this setting is needed [4,5]. In general, older adults with cancer need an appropriate preliminary evaluation before treatment, to avoid under- and over-treatment [6]. The efficacy of oncologic treatment can be affected by several limiting factors, e.g. comorbidity, polypharmacy and drug interactions, cognitive and functional status, as well as social environment [7]. For these reasons, several geriatric assessment tools for the stratification of cancer treatment have been proposed [8]. However, there is no consensus on the most useful tool for predicting prognosis and toxicity risk in older adults with cancer [9,10].
⁎ Corresponding author at: Geriatrics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Via Savi 10, 56126 Pisa, Italy. ⁎⁎ Corresponding author. E-mail addresses: [email protected] (G. Pasqualetti), [email protected] (A. Antonuzzo). 1 SA and RA equally contributed to the manuscript as first authors
https://doi.org/10.1016/j.jgo.2019.09.010 1879-4068/© 2019 Published by Elsevier Ltd.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010
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A. Sbrana et al. / Journal of Geriatric Oncology xxx (2019) xxx
The Multi-Prognostic Index (MPI) is an effective tool, derived from a standard comprehensive geriatric assessment (CGA), validated in many clinical settings to stratify prognosis of older adults with cancer [11,12]. MPI, which includes 63 items distributed in 8 domains, emerged as a highly accurate and well-calibrated tool for prediction of one-year mortality in older adults with cancer, enhancing clinical decision making [13]. However, so far, no prospective studies have been carried out to evaluate the usefulness of MPI to better stratify older adults with cancer eligible for immunotherapy. Thus, the current study aims to test MPI as a screening tool to enhance clinical decision making in older adults with cancer who are candidates for immunotherapy. 2. Patients and Methods The population enrolled in the study was composed of older adults with cancer consecutively attending the Oncology Day Hospital of the University Hospital of Pisa, from January 2017 to May 2018. Patients were candidate to receive immunotherapy due to advanced or metastatic cancer. The study protocol was approved by the Ethic Committee of the University Hospital of Pisa, Italy. All the patients were assessed by the Eastern Cooperative Oncology Group Performance Status (ECOG PS) scale, to establish their eligibility for receiving an oncologic active treatment [14]. Demographic, oncologic, and geriatric data were also collected and registered. Specifically, oncologic data included information about the primary site of the tumor, the sites of metastases, the type of immunotherapeutic agent, the line treatment, and the treatments undertaken prior to immunotherapy. Comprehensive geriatric assessment included the following scales: Short Physical Performance Battery (SPPB), Activity of Daily Living (ADL), Instrumental Activity of Daily Living (IADL), Short Portable Mental Status Questionnaire (SPMSQ), Mini-Nutritional Assessment (MNA), Cumulative Illness Rating Scale (CIRS) and Exton-Smith Scale (ESS) [14]. Polypharmacy and social support network were also recorded. The SPPB measures balance, gait speed and lower limb strength and endurance. Among non-disabled older adults living in the community, the SPPB was highly predictive of subsequent disability and, several studies have evaluated outcomes using the SPPB as a risk assessment of frailty in older adults [15]. We classified patients as follows [16]: frail (SPPB score of 1–4), prefrail (SPPB score of 5–8) and fit (SPPB score of 9–12). In addition to SPPB, the MPI score, an accurate index of frailty as well as a validated marker of short- and long-term mortality, was calculated incorporating the above geriatric measures as well as polypharmacy and living arrangement [2–18]. A detailed description of MPI calculation is available in Supplementary Material. Efficacy of immunotherapy as well as safety endpoints; namely, overall survival, progression-free survival, and treatment-related toxicities, with a particular attention to NCI-CTCAE (National Cancer Institute Common Terminology Criteria for Adverse Events, Version 4.03: June 14, 2010) grade 3 and 4 toxicities were analyzed and recorded throughout follow-up. 2.1. Statistical Analysis Statistical analysis was performed by using SPSS 21.0 statistical software package (SPSS Inc., Chicago, IL). Continuous variables are expressed as mean ± SD, ordinal variable as median and range and categorical variables as percentage. Descriptive statistics of the entire cohort was firstly performed. Independent sample t-test was used for normally distributed data and, Mann-Whitney U Test for non-parametric data. Univariate Cox regression analysis, adjusted for covariates resulting significantly different at baseline on the comparison among groups, was performed to identify factors significantly associated with overall survival. Then, Coxproportional regression model was applied for survival analysis. Statistical significance was given for p b 0.05.
3. Results We enrolled 79 older adults with cancer [73.4% men, mean age (± SD) 74.2 ± 6.1 years] with the following diagnosis: melanoma (51.6%), non-small cell lung cancer (NSCLC) (25.3%), renal cell cancer (12.7%), urothelial cancer (8.9%) and Merkel cell carcinoma (1.2%). According to ECOG PS, most patients had a good performance status, either 0 (40.5%) or 1 (48.1%) while, 9 patients (11.4%) had a poor performance status (2 or 3) (Table 1). Patients received immunotherapy as first- (45.6%) or second-line therapy (45.6%); only few subjects received the treatment as third- or further-line therapy (8.8%). Patients mainly received anti-PD1 (n = 67, 84.8%) and anti-PDL1 antibodies (n = 8, 10.1%), whilst a minority received ipilimumab, an anti-CTLA4 antibody (n = 4, 5.1%). Median follow-up was 7 months (range 1–35). Data on CGA are reported in Table 2. At SPPB examination only 36.7% of the patients were classified as fit (score 9–12) while, 46.8% as pre-frail (score 5–8) and 16.5% as frail (score 1–4). Also, by SPPB score stratification no significant differences were obtained in terms of overall survival (data not shown). Conversely, patients showed a good clinical status in most of the other CGA scales: 68.4% with 5–6 ADL activities,76.0% with 0–2 errors at SPMSQ, 82.3% without bedsore risk (ESS score 16–20) and 40.5% with normal nutritional state (MNA score 24–30). By Cox regression analysis, patients with an elevated burden of comorbidities (CIRS-C ≥ 3) showed a significantly higher risk of mortality [HR 2.32 (95% C.I., 1.09–4.94), p b 0.05] while, a good nutritional state (MNA ≥ 24) significantly reduced it [HR 0.36 (95% C.I., 0.02–0.57), p b 0.05]. No significant correlation was obtained with the other CGA items (data not shown). In order to increase the overall prognostic value of CGA, the MPI score was then calculated. Table 3 summarizes the oncologic and geriatric evaluation of patients clustered by MPI tertiles. In detail, apart from the expected variable stratification of each CGA item, significant differences in terms of ECOG PS and treatment lines were obtained (p b 0.05, for both). Moreover, patients' overall survival resulted significantly different according to MPI tertiles (Fig. 1). Specifically, patients in the first MPI tertile showed 76% increased survival rate than those in the 2nd tertile [HR 1.76 (95% C.I., 0.49–6.31), p b 0.05], which became 5 time longer respect to those in the 3rd tertile [HR 5.33 (95% C.I., 1.68–16.89), p b 0.01].
Table 1 Baseline clinical characteristics of the whole cohort of patients (n=79). Age (years) Women Primary tumor site • Melanoma • Non-Small Cell Lung Cancer (NSCLC) • Renal cell cancer • Urothelial cancer • Merkel cell carcinoma Setting at the moment of diagnosis • Local • Locally advanced • Metastatic Line of therapy • First • Second • Third or more Type of drug • Anti-PD1 antibodies (pembrolizumab, nivolumab) • Anti-PDL1 antibodies (avelumab, durvalumab) • Anti-CTLA4 antibodies ECOG PS • 0 • 1 • 2–3
74.2 ± 6.1 21 (26.6%) 41 (51.9%) 20 (25.3%) 10 (12.7%) 7 (8.9%) 1 (1.2%) 23 (29.1%) 27 (34.2%) 29 (36.7%) 36 (45.6%) 36 (45.6%) 7 (8.8%) 67 (84.8%) 8 (10.1%) 4 (5.1%) 32 (40.5%) 38 (48.1%) 9 (11.4%)
ECOG PS: Eastern Cooperative Oncology Group Performance Status. All the data are reported as mean ± SD or number (%), as appropriate.
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A. Sbrana et al. / Journal of Geriatric Oncology xxx (2019) xxx Table 2 Baseline comprehensive geriatric assessment of the whole cohort of patients (n=79). ADL • 5–6 • 2–4 • 0–1 IADL • 6–8 • 3–5 • 0–2 SPMSQ • 0–2 • 3–4 • 5–7 • 8–10 MNA • 24–30 • 18–23.5 • 0–17 CIRS-C CIRS-S Number of drugs • 0 • 1–3 • 4–6 • 7 or more ESS • 0–5 • 6–10 • 11–15 • 16–20 SPPB • 1–4 (frail) • 5–8 (pre-frail) • 9–12 (fit) Living arrangements • Alone • With their family • Dedicated health structure MPI
54 (68.4%) 20 (25.3%) 5 (6.3%) 16 (20.3%) 42 (53.1%) 21 (26.6%) 60 (76%) 10 (12.6%) 6 (7.6%) 3 (3.8%) 32 (40.5%) 37 (46.8%) 10 (12.7%) 2 (1–6) 1.46 (0.46–2.23) 9 (11.4%) 43 (54.4%) 16 (20.3%) 11 (13.9%) 1 (1.2%) 3 (3.8%) 10 (12.7%) 65 (82.3%)
Thus, the prognostic evaluation of life expectancy is a corner stone in the pretreatment clinical classification of older patients with cancer eligible for immunotherapy. In this setting, there is growing need to select patients in order to treat those who might really receive clinical benefit, avoiding useless treatments as well as serious drug-related adverse events. Phase 3 trials that led to the Drug Regulatory Agencies' approval of checkpoint inhibitors included only a small number of older patients, making the choice of treatment somewhat arbitrary in older patients [22]. Thus, real-life data with prognostic evaluation of older adults with cancer are needed to better balance harm–and cost–benefits of immunotherapy. Over the last years, some generic prognostic scores for the risk stratification of older patients with cancer have been proposed but, there is no consensus on the most appropriate tool for predicting prognosis and toxicity risk [9,23,24]. MPI was previously validated in older hospitalized patients, suffering from major diseases, including several types of cancer [25,26], with a significant higher predictive power than other widely used frailty indexes [10–13,15–18]. In a prospective clinical trial, enrolling older adults with locally advanced or metastatic solid cancers, MPI outperformed multidimensional geriatric assessment in predicting mortality [27]. Moreover, in a large cohort of older patients with cancer, a modified multidimensional prognostic index (Onco-MPI) for mortality prediction has been developed [13]. Different from the classical Table 3 Patients' clinical characteristics, overall survival and drugs' toxicity according to MPI tertiles.
13 (16.5%) 37 (46.8%) 29 (36.7%) 17 (21,5%) 58 (73.4%) 4 (5.1%) 0.25 (0.06–0.75)
ADL: Activities of Daily Living; IADL Instrumental Activities of Daily Living; SPMSQ: Short Portable Mental Status Questionnaire; MNA: Mini-Nutritional Assessment; CIRS-C: Cumulative Illness Rating Scale-Comorbidity; CIRS-S: Cumulative Illness Rating Scale-Severity; ESS: Exton-Smith Scale; SPPB: Short Physical Performance Battery; MPI: Multi-Prognostic Index. Data are reported as number (%) or median (range) as appropriated.
Forty-three patients (54.5%) experienced at least one toxicity from the treatment, although only 6 (7.6%) suffered from severe toxicity (i.e. grade 3 or 4 according to NCI-CTCAE criteria). In detail, patients mainly experienced dermatologic adverse events [13 patients, (16.5%), mostly skin rashes and pruritus], diarrhea [12 patients (15.2%)] and endocrinological toxicity [10 patients (12.7%), mostly hypothyroidism]. 4. Discussion The current study shows that MPI is a useful tool for prognostic stratification of older patients with either local or advanced cancer, treated with immunotherapy. Indeed, patients with the highest MPI score experienced the worst overall survival, with a 5-fold increased risk of mortality. According to literature [19], among the CGA domains by which MPI derives, only nutritional state and the burden of comorbidities emerged as independent risk factors of overall survival. Partially at odds with prior studies, no significant association was also observed between polypharmacy and overall survival [20]. However, previous reported data are limited by study confounders, inconsistent definitions for polypharmacy, heterogeneous cancer types and stages, and the complex relationship between medication regimens and outcomes. Finally, in our cohort the safety profile of immunotherapy does not differ from that observed in younger patients and phase 3 clinical trials [3,7,21]. Several studies highlighted immunotherapy as a valuable option for the treatment of adults affected by several types of cancer (melanoma, NSCLC, and genitourinary cancers) [21]. Immunotherapy is not exempt from toxicities, especially in case of high burden of comorbidity.
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First tertile (0.06–0.30)
Second tertile (0.31–0.52)
Third tertile (0.53–0.75)
28 74.2 ± 6.7 9 (32.1%)
32 75.8 ± 4.6 5 (15.6%)
19 74.9 ± 5.8 7 (36.8%)
17 (60.7%) 5 (17.9%) 4 (14.3%) 2 (7.1%) 0
13 (40.6%) 12 (37.5%) 5 (15.7%) 1 (3.1%) 1 (3.1%)
11 (57.9%) 3 (15.8%) 1 (5.2%) 4 (21.1%) 0
Setting at the moment of diagnosis • Local 10 (35.7%) • Locally Advanced 10 (35.7%) • Metastatic 8 (28.6%)
9 (28.1%) 7 (21.9%) 16(50%)
4 (21.1%) 10 (52.6%) 5 (26.3%)
Line of therapy⁎ • First • Second • Third or more
15 (53.6%) 10 (35.7%) 3 (10.7%)
12 (37.5%) 19 (59.4%) 1 (3.1%)
9 (47.4%) 7 (36.8%) 3 (15.8%)
17 (60.7%) 11 (39.3%) 0 5 (5–6) 5 (3–8) 0 (0–5)
7 (21.9%) 23 (71.9%) 2 (6.2%) 5 (3–6) 3 (1–5) 0 (0–4)
8 (42.1%) 4 (21.1%) 7 (36.8%) 2 (0–4) 1 (0–3) 5 (0−10)
21 (75%) 7 (25%) 0% 2 (1–4) 1.4 ± 0.3 20 (15–20) 28.8 ± 2.9 18 (66.7%) 3 (10.7%)
8 (25%) 21 (65.6%) 3 (9.4%) 2.5 (2–6) 1.5 ± 0.2 20 (13−20) 23 ± 3.1 15 (46.9%) 0
3 (15.8%) 9 (47.4%) 7 (36.8%) 3 (1–5) 1.7 ± 0.3 13(5–20) 13.7 ± 2.8 10 (52.6%) 3 (15.8%)
Total number of patients Age Female Primary tumor • Melanoma • NSCLC • Kidney • Urothelial • Merkel
ECOG PS⁎ • 0 • 1 • 2–3 ADL IADL SPMSQ MNA • 30-24 • 23.5-18 • 17-0 CIRS-C CIRS-S ESS Overall survival (months) Overall toxicities G3-G4 toxicities
NSCLC: Non-Small Cell Lung Cancer; ECOG PS: Eastern Cooperative Oncology Group Performance Status; ADL: Activities of Daily Living; IADL Instrumental Activities of Daily Living; SPMSQ: Short Portable Mental Status Questionnaire; MNA: Mini-Nutritional Assessment; CIRS-C: Cumulative Illness Rating Scale-Comorbidity; CIRS-S: Cumulative Illness Rating Scale-Severity ESS: Exton-Smith Scale; MPI: Multi-Prognostic Index; G3-G4: Grade 3 – Grade 4. Data are reported as mean ± SD, median (range) or number (%) as appropriate. ⁎ p b 0.05.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010
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1.0 MPI 1 MPI 2
0.8
Cum Survival
MPI 3
0.6
0.4
0.2
0.0 0
5
10
15
20
25
30
Time (Months) MPI 1 MPI 2 MPI 3
0 28 32 19
5 20 (2) 18 (3) 13 (4)
10 13 (3) 10 (4) 4 (7)
15 7 (4) 7 (5) 2 (9)
20 5 (4) 2 (6) 2 (10)
25 4 (4) 1 (6) 1 (10)
30 1 (4) 1 (6) 0 (11)
Number at Risk (cumulative number of deaths) during follow-up Fig. 1. Cox-adjusted overall survival curve according to MPI (Multi-Prognostic Index) tertiles.
MPI, it included the following items to evaluate functional, cognitive and nutritional status: ECOG PS (instead of ESS), MMSE (instead of SPMSQ) and BMI (instead of MNA), respectively. Interestingly, OncoMPI appeared a highly accurate and well-calibrated predictive tool for one-year mortality and, as such, it might be useful for clinical decision making in older adults with cancer [13]. Nonetheless, older patients enrolled in that study were not selected on the basis of specific cancer treatments and, no data were reported regarding immunotherapy [13]. The main strength of the current study relies both on the age of enrolled patients (averaging 74 years), which is uncommon in clinical trials, and on the fact that, for the first time, MPI has been utilized for clinical stratification of older adults with advanced/metastatic cancer, eligible for immunotherapy. It is worth noting that our cohort was characterized by the presence of a small proportion of patients with poor performance status by ECOG PS (11.4% with score 2–3) in the face of a high prevalence of frail or pre-frail patients by SPPB (63.3%), malnourished or at risk of malnutrition by MNA (59.5%). Nonetheless, the present study has some limitations. Firstly, the chosen cancer population displayed heterogeneity in terms of primary tumor and different line of treatments. Moreover, the small number of adults with NSCLC may represent a potential weakness of the study, although they account for almost one fourth of the whole cohort. The relative over-representation of melanoma is due to the difference of immunotherapy indication in NSCLC and melanoma in Italy in the observation period. Indeed, adults with melanoma could receive immunotherapy both in first and second line for metastatic disease, whereas adults with NSCLC could receive it only in second line for metastatic disease. By MPI stratification, older adults with NSCLC were more frequently allocated in the second MPI tertile, also because of the high burden of comorbidity (mean CIRS-C 4.2 Vs 1.9 in NSCLC patients of the 1st tertile). However, older adults with melanoma or NSCLC exhibited similar
survival trends, although without statistical significance in the latter group of patients. Overall, our data do not allow final conclusions regarding potential heterogeneous usefulness of MPI stratification in different cancer types. Given these premises, differences in tumor diagnosis based on MPI or CGA scales, did not interfere with the overall survival analysis and, routine use of MPI stratification may represent a reliable and effective tool for selecting older adults with cancer receiving immunotherapy. Indeed, older adults are often affected by a variable degree of comorbidity and disability, which represents a crucial clinical challenge for oncologists. In a multidisciplinary team, patients that are found to be at risk in a pre-screening test (e.g. G8 questionnaire [25]) might be submitted to comprehensive geriatric assessment with calculation of the MPI score. Thus, MPI score allows to stratify older adults in relation to both frail degree and survival expectancy, making it possible to exclude from treatment frail patients with reduced life expectancy (in our series MPI N 0.50). Patients with intermediate MPI score (between 0.31 and 0.50, in our cohort) could be more closely monitored, with a case-by-case tailored therapeutic decision. It might be interesting to explore in future studies conducted in patients with higher MPI score whether an early geriatric assessment with clinical interventions (directed to patients' needs) may yield to a better survival, thus allowing the use of immunotherapy even in this setting. In conclusion, MPI score is an effective screen for treatment stratification of older patients eligible for checkpoint inhibitors, encouraging its routinely use in everyday clinical practice. Our findings support the use of immunotherapy in older patients with advanced cancer, provided that baseline comprehensive geriatric assessment is performed. However, further studies with larger populations are needed in order to achieve a conclusive remark on the efficacy of MPI in diverse underlying tumor types.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010
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Author Contribution Statement Pasqualetti G, Sbrana A, Linsalata G, Antognoli R, Paolieri F, Bloise F, Okoye C and Calsolaro V equally contributed to study conduction and data collection. Pasqualetti G, Sbrana A, Linsalata G, Antognoli R and Monzani F participated to data analysis and manuscript writing. Ricci S, Antonuzzo A and Monzani F designed the study and participated to interpretation of results. Monzani F, Sbrana A and Antognoli R revised the manuscript. Declaration of Competing Interest Nothing to declare. Acknowledgements Nothing to declare. References [1] Hurria A, Dale W, Mooney M, Rowland JH, Ballman KV, Cohen HJ, et al. Designing therapeutic clinical trials for older and frail adults with cancer: U13 conference recommendations. J Clin Oncol 2014;32:2587–94. https://doi.org/10.1200/JCO.2013.55. 0418. [2] Hurria A, Mohile S, Gajra A, Klepin H, Muss H, Chapman A, et al. Validation of a prediction tool for chemotherapy toxicity in older adults with cancer. J Clin Oncol 2016; 34:2366–71. https://doi.org/10.1200/JCO.2015.65.432. [3] Nishijima TF, Shachar SS, Nyrop KA, Muss HB. Safety and tolerability o PD-1/PD-L1 inhibitors compared with chemotherapy in patients with advanced cancer: a meta-analysis. Oncologist 2017;22:470–9. https://doi.org/10.1634/theoncologist. 2016-0419. [4] Poropatich K, Fontanarosa J, Samant S, Sosman JA, Zhang B. Cancer immunotherapies: are they as effective in the elderly? Drugs Aging 2017;34:567–81. https:// doi.org/10.1007/s40266-017-0479-1. [5] American Society of Clinical Oncology Educational Book vol. 38 400–414, [10.1200/ EDBK_201435 [accessed 14 April 2019]]. [6] Le Saux O, Falandry C, Gan HK, You B, Freyer G, Péron J. Inclusion of elderly patients in oncology clinical trials. Ann Oncol 2016;27:1799–804. https://doi.org/10.1093/ annonc/mdw259. [7] Muchnik E, Loh KP, Strawderman M, Magnuson A, Mohile SG, Estrah V, et al. Immune checkpoint inhibitors in real-world treatment of older adults with nonsmall cell lung cancer. J Am Geriatr Soc 2019;67(5):905–12. https://doi.org/10. 1111/jsg.15750. [8] Pamoukdjian F, Liuu E, Caillet P, Gisselbrecht M, Herbaud S, Boudou-Rouquette P, et al. Geriatric assessment and prognostic scores in older cancer patient: additional support to the therapeutic decision? Bull Cancer 2017;104:946–55. https://doi.org/ 10.1016/j.bulcan.2017.10.004. [9] Pamoukdjian F, Liuu E, Caillet P, Herbaud S, Gisselbrecht M, Poisson J, et al. How to optimize cancer treatment in older patients: an overview of available geriatric tools. Am J Clin Oncol 2019;42:109–16. https://doi.org/10.1097/COC. 0000000000000488. [10] Dent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: a review. Eur J Intern Med 2016;31:3–10.
5
[11] Pilotto A, Ferrucci L, Franceschi M, D’Ambrosio LP, Scarcelli C, Cascavilla L, et al. Development and validation of a multidimensional prognostic index for one-year mortality from comprehensive geriatric assessment in hospitalized older patients. Rejuvenation Res 2008;11:151–61. https://doi.org/10.1089/rej.2007.0569. [12] Giantin V, Falci C, De Luca E, Valentini E, Iasevoli M, Siviero P, et al. Performance of the multidimensional geriatric assessment and multidimensional prognostic index in predicting negative outcomes in older adults with cancer. Eur J Cancer Care (Engl) 2018 Jan;27(1). https://doi.org/10.1111/ecc.12585. [13] Brunello A, Fontana A, Zafferri V, Panza F, Fiduccia P, Basso U, et al. Development of an oncological-multidimensional prognostic index (Onco-MPI) for mortality prediction in older cancer patients. J Cancer Res Clin Oncol 2016;142:1069–77. https://doi. org/10.1007/s00432-015-2088-x. [14] Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, et al. Toxicity and response criteria of the eastern cooperative oncology group. Am J Clin Oncol 1982;5:649–55. [15] Gary R. Evaluation of frailty in older adults with cardiovascular disease: incorporating physical performance measures. J Cardiovasc Nurs 2012;27(2):120–231. https:// doi.org/10.1097/JCN.0b013e318239f4a4. [16] Chiarantini D, Volpato S, Sioulis F, Bartalucci F, et al. Lower extremity performance measures predict long-term prognosis in older patients hospitalized for heart failure. J Card Fail 2010;16(5):390–5. https://doi.org/10.1016/j.cardfail.2010.01.004 [Epub 2010 Mar 3]. [17] Elsawy B, Higgins KE. The geriatric assessment. Am Fam Physician 2011;83(1): 48–56 Jan1. [18] Pilotto A, Addante F, Ferrucci L, Leandro G, D’Onofrio G, Corritore M, et al. The multidimensional prognostic index predicts short- and long-term mortality in hospitalized geriatric patients with pneumonia. J Gerontol A Biol Sci Med Sci 2009;64:880–7. https://doi.org/10.1093/gerona/glp031. [19] Martucci RB, Barbosa MV, D’Almeida CA, Rodrigues VD, Bergmann A, de Pinho NB, et al. Undernutrition as independent predictor of early mortality in elderly cancer patients. Nutrition 2017;34:65–70. https://doi.org/10.1016/j.nut.2016.09.011. [20] Nightingale G, Skonecki E, Boparai MK. The impact of polypharmacy on patient outcomes in older adults with cancer. Cancer J 2017;23:211–8. [21] Dine J, Gordon R, Shames Y, Kasler MK, Barton-Burke M. Immune checkpoint inhibitors: an innovation in immunotherapy for the treatment and management of patients with cancer. Asia Pac J Oncol Nurs 2017;4:127–35. https://doi.org/10.4103/ apjon.apjon_4_17. [22] Marrone KA, Forde PM. Cancer immunotherapy in older patients. Cancer J 2017;23: 219–22. https://doi.org/10.1097/PPO.0000000000000268. [23] Pilotto S, Sperduti I, Novello S, Peretti U, Milella M, Facciolo F, et al. Risk stratification model for resected squamous-cell lung cancer patients according to clinical and pathological factors. J Thorac Oncol 2015;10:1341–8. https://doi.org/10.1097/JTO. 0000000000000628. [24] Baijal P, Periyakoil V. Understanding frailty in cancer patients. Cancer J 2014;20: 358–66. https://doi.org/10.1097/PPO.0000000000000068. [25] Pilotto A, Rengo F, Marchionni N, Sancarlo D, Fontana A, Panza F, et al. Comparing the prognostic accuracy for all-cause mortality of frailty instruments: a multicentre 1-year follow-up in hospitalized older patients. PLoS One 2012;7:e29090. https:// doi.org/10.1371/journal.pone.0029090. [26] Giantin V, Valentini E, Iasevoli M, Falci C, Siviero P, De Luca E, et al. Does the multidimensional prognostic index (MPI), based on a comprehensive geriatric assessment (CGA), predict mortality in cancer patients? results of a prospective observational trial. J Geriatr Oncol 2013;4:208–17. https://doi.org/10.1016/j.jgo. 2013.04.008. [27] Bellera CA, Rainfray M, Mathoulin-Pélissier S, Mertens C, Delva F, Fonck M, et al. Screening older cancer patients: first evaluation of the G-8geriatric screening tool. Ann Oncol 2012;23:2166–72. https://doi.org/10.1093/annonc/mdr587.
Please cite this article as: A. Sbrana, R. Antognoli, G. Pasqualetti, et al., Effectiveness of Multi-Prognostic Index in older patients with advanced malignancies treated with im..., J Geriatr Oncol, https://doi.org/10.1016/j.jgo.2019.09.010