Immune checkpoint inhibitors (ICIs) produce durable clinical response for a subset of patients with cancer, but many individuals do not benefit, and many more develop autoimmune toxicity.1–4 Predictive markers are needed to optimize patient selection for treatment. Several studies suggest a link between baseline body mass index (BMI) and response to ICIs.5–9 Obesity predisposes to immune system dysfunction, which means that it could simultaneously represent an opportunity to reinvigorate the anticancer immune response through checkpoint blockade.10 However, there are substantial discordances in the data including variation in the magnitude and direction of the reported associations. In fact, we recently reported heterogenous trends in the relationship between baseline BMI and ICI response in melanoma.11 Specifically, we found that overweight and obese patients receiving combination immunotherapy had a significant progression-free survival (PFS) benefit, but there was no difference in PFS in those who received anti-programmed cell death protein 1 or anti-cytotoxic T-lymphocyte-associated protein 4 monotherapy. Moreover, there was no overall survival (OS) benefit seen in overweight patients or patients with obesity.

One limitation to most of the extant literature is that BMI was evaluated at a single point in time. Static assessments fail to capture dynamic changes in weight and nutrition. Some patients may have a lower BMI in the setting of worsening disease burden. In this case, reverse causation bias could lead to a distorted interpretation of the data whereby obesity is misconstrued as protective, when in fact it is declining nutritional status and concomitant systemic inflammation that dampen ICI response. BMI reflects nutritional status, but we postulate that pretreatment BMI changes prior to the start of treatment provide a more comprehensive picture than baseline values taken at the time of treatment initiation. In this study, we hypothesized that worsening of nutritional status before the start of immunotherapy, rather than baseline BMI, might negatively impact immunotherapy response. To test this hypothesis, we investigated the association between pretreatment BMI trends in the month before treatment initiation, baseline BMI category and immunotherapy response in a cohort of patients with advanced cancer. Given that nutritional status can also be assessed using serum laboratory values, we concurrently investigated the predictive potential of the baseline prognostic nutritional index (PNI), which is a validated tool with established prognostic utility in patients with cancer.12–17

In this study of patients with advanced cancer who received immune checkpoint inhibition, we tested the association between response to immunotherapy and three different nutritional indices: BMI changes prior to the start of treatment, baseline BMI category and baseline PNI. We observed that patients whose BMI decreased in the 15–45 days before treatment initiation suffered worse treatment outcomes, as did individuals with a low baseline PNI. There were no significant associations between baseline BMI category and ICI response. Our findings demonstrate two important points. First, pretreatment malnutrition, which is a modifiable variable, portended worse outcomes following checkpoint blockade independent of all possible confounding factors. Second, dynamic BMI trajectories, rather than static measurements, correlated with immunotherapy response. Taken together, these results underscore the importance of optimizing nutritional status as patients prepare for and receive ICI, and highlight the potential value of using BMI trends rather than baseline BMI category to characterize nutritional state.

The predictive value of nutritional status for ICI efficacy is not well-established, which is in contrast with the plethora of data supporting the association between malnutrition and overall survival.21–23 Only one recent study reported that patients with non-small cell lung cancer (NSCLC) with a PNI ≤45.5 had significantly worse ORR, DCR and PFS as compared with patients with PNI >45.5.24 Yet, there are several reasons that poor nutritional status could impair ICI effectiveness. First, hypercatabolic activity in response to malnutrition could lead to accelerated clearance of monoclonal antibodies, which are primarily eliminated via catabolic degradation.25 In fact, one group showed that decreased OS in patients with higher pembrolizumab clearance coincided with cancer cachexia, which speaks to the potential negative impact of elevated protein turnover.26 Second, chronic inflammation associated with malnutrition could paradoxically suppress activation of the adaptive immune system. Recent preclinical data show that heightened levels of the proinflammatory cytokine interleukin-6 (IL-6) induce systemic glucocorticoid release.27 This increased basal expression of endogenous steroids might dampen immune cell activity and consequently abrogate checkpoint inhibitor efficacy, as has been seen following the administration of exogenous steroids.28

Going forward, it will be important to determine whether and to what degree the optimization of nutritional status augments ICI response. Multiple prior studies, although not all, have shown that nutritional interventions are associated with improved survival in patients undergoing chemotherapy and radiation therapy, which suggests that dietary support can potentially yield meaningful clinical change.29–32 However, evidence suggests that the benefits are limited to patients at earlier stages of malnutrition as opposed to individuals with cancer cachexia, which is characterized by impaired anabolic signaling.33 34 In our study, we found that the subset of patients who were malnourished but did not meet criteria for cachexia still had worse treatment outcomes than patients who were not malnourished. Since these individuals are in the critical period of reversible malnutrition, they stand to benefit most from nutritional support and should be referred for dietary consultation.19 35 As expected, there is no catch-all strategy to managing weight loss and malnutrition in patients with cancer. Instead, it requires a multifaceted approach that includes but is not limited to regular screening, nutrition counseling, treatment of any symptoms that might impair food intake, prescription of oral nutritional supplements and possibly the use of pharmacological agents to improve appetite.36

It is noteworthy that pretreatment BMI trends, but not baseline BMI category, correlated with ICI response in this cohort of patients. We suspect that this is because longitudinal measurements capture dynamic changes in body composition and therefore more accurately reflect nutritional status than static measurements. Our findings call into question the predictive utility of baseline BMI. In fact, the extant literature already presents a mixed picture of pretreatment BMI category as a marker of response. For instance, some groups show that there is a linear relationship between increasing BMI and improving outcomes, while others report a U-shaped pattern, in which overweight but not obese patients exhibit mortality benefits. Additionally, there are discrepancies in the end points purportedly associated with BMI. Rogado et al found higher ORR and improved PFS in excess weight patients, whereas Richteg et al reported better ORR but no differences in PFS or OS.6 37 We evaluated multiple different clinical end points but could find no association between baseline BMI category and response to immunotherapy.

We showed that change in pretreatment BMI was associated with immunotherapy treatment outcomes when analyzed as either a categorical (decrease vs no decrease) or continuous variable. This suggests that even minor amounts of weight loss could have clinical implications for candidates of checkpoint blockade. That said, small decreases in BMI do not necessarily represent a true worsening of clinical status. There are a number of potential confounders including differences in measurement or intentional weight loss. Future studies should employ uniform protocols for anthropometric assessments. Since that was not possible for our study, we attempted to account for these cofounders by selectively analyzing the subset of patients who suffered ≥2% decrease in pretreatment BMI. We found that these individuals had significantly worse treatment outcomes following checkpoint blockade, which reinforces the validity of clinically significant decreases in BMI as a marker of response.

There are several limitations to this study. First, the PNI does not distinguish nutritional status from systemic inflammation. This is because inflammatory cytokines such as IL-6 regulate hepatocyte production of albumin, so hypoalbuminemia can be attributed to malnutrition, inflammation or both.12 Measuring C reactive protein and IL-6 levels could help determine whether and to what degree low PNI is due to systemic inflammation, however, we did not have these data available for analysis. In a similar vein, reporting prealbumin and transferrin levels could inform whether hypoalbuminemia reflects malnutrition. Albumin has a half-life of 20 days, but prealbumin and transferrin have shorter half-lives (2–3 and 10 days, respectively) and would provide a more comprehensive and up-to-date assessment of nutritional status.38 There are other prognostic scoring systems that can be used to select patients for treatment such as the Royal Marsden Hospital (RMH) and Gustave Roussy Immune (GRIm) Scores.39 40 The GRIm-Score includes neutrophil-to-lymphocyte ratio and therefore has the potential to be more clinically relevant than PNI, which only incorporates the quantity of lymphocytes. However, both of these scoring systems include lactate dehydrogenase (LDH), and we only had pretreatment LDH values for a subset of our cohort, so we could not compare their predictive utility to that of the PNI. Finally, the diagnostic criteria for cachexia include weight loss ≥5% over the past 6 months, but we did not have information regarding patient weight this far before starting immunotherapy. Based on the available data, we assessed weight changes in the 15–45 days prior to treatment initiation. It is therefore possible that we did not identify all patients in the cohort who meet criteria for cachexia. Future studies need to include long-term weight trends starting at least 6 months before therapy in order to more precisely determine whether precachexia is associated with worse ICI response and whether outcomes improve following nutritional support in these patients.

In conclusion, we show that pretreatment BMI changes and baseline PNI are associated with immunotherapy response in patients with advanced cancer. These indicators of malnutrition are calculated using information that is readily available through routine standard of care and thus have potential for clinical implementation. Our findings need to be replicated in prospective studies in conjunction with other nutritional intake metrics, anthropometric measurements and laboratory markers in order to further elucidate the links between nutritional status and immunotherapy response. It is equally important to determine whether optimizing pretreatment nutritional status leads to improvement in clinical outcomes following checkpoint blockade.