Macroangiopathy is a positive predictive factor for response to immunotherapy

The Emerging Role and Clinical Applications of Morphomics in Diagnostic Imaging

Analytic morphomics refers to the accurate measurement of specific biological markers of human body composition in diagnostic medical imaging. The increasing prevalence of disease processes that alter body composition including obesity, cachexia, and sarcopenia has generated interest in specific targeted measurement of these metrics to possibly prevent or reduce negative health outcomes. Typical morphomic measurements include the area and density of muscle, bone, vascular calcification, visceral fat, and subcutaneous fat on a specific validated axial level in the patient's cross-sectional diagnostic imaging. A distinct advantage of these measurements is that they can be made retrospectively and opportunistically with pre-existing datasets. We provide a narrative review of the current state of art in morphomics, but also consider some potential future directions for this exciting field. Imaging based quantitative assessment of body composition has enormous potential across the breadth and scope of modern clinical practice. From risk stratification to treatment planning, and outcome assessment, all can be enhanced with the use of analytic morphomics. Moreover, it is likely that many new opportunities for personalized medicine will emerge as the field evolves. As radiologists, embracing analytic morphomics will enable us to contribute added value in the care of every patient.

Prognostic value of body composition on survival outcomes in melanoma patients receiving immunotherapy

Objective

The influence of body composition on the effectiveness of immune checkpoint inhibitors (ICIs) in patients with melanoma is still uncertain in clinical practice. Therefore, the objective of this study was to examine the potential association between body composition and clinical outcomes in patients with melanoma undergoing ICIs treatment.

Methods

A systematic literature search was performed across several databases, including PubMed, Embase, Cochrane Library and Google Scholar, to gather relevant studies. The primary outcomes of interest were overall survival (OS) and progression-free survival (PFS), assessed by hazard ratios (HR). Secondary outcomes, such as adverse events (AE), were evaluated using odds ratios (OR).

Results

This meta-analysis comprised ten articles involving a total of 1,283 patients. Systemic analysis of all collected evidence revealed that body composition, including low skeletal muscle index (SMI) (OS: HR = 1.66, 95% CI = 1.13-2.43, p = 0.010; PFS: HR = 1.28, 95% CI = 1.06-1.55, p = 0.009), high subcutaneous adipose tissue density (SMD) (OS: HR = 1.93, 95% CI = 1.09-3.44, p = 0.025; PFS: HR = 1.31, 95% CI = 1.06-1.63, p = 0.012), and sarcopenia (OS: HR = 1.25, 95% CI = 1.03-1.51, p = 0.022; PFS: HR = 1.25, 95% CI = 1.03-1.51, p = 0.022), were significantly associated with OS and PFS in melanoma patients treated with ICIs. However, these markers did not show a significant association with treatment-related adverse events. Interestingly, no significant correlation was found between visceral fat index (VFI) (OS: HR = 0.71, 95% CI = 0.29-1.76, p = 0.462; PFS: HR = 0.98, 95% CI = 0.93-1.02, p = 0.274) and OS or PFS in melanoma patients under ICIs treatment.

Conclusion

Body composition was found to be associated with decreased treatment response and lower long-term efficacy in patients with melanoma undergoing immune checkpoint inhibitor (ICI) therapy. However, it is important to note that body composition did not appear to contribute to increased incidence of adverse events in these patients.

Contribution of skeletal muscle to cancer immunotherapy: A focus on muscle function, inflammation, and microbiota

Sarcopenia, characterized by degenerative and systemic loss of skeletal muscle mass and function, is a multifactorial syndrome commonly observed in individuals with cancer. Additionally, it represents a poor nutritional status and indicates possible presence of cancer cachexia. Recently, with the extensive application of cancer immunotherapy, the effects of sarcopenia/cachexia on cancer immunotherapy, have gained attention. The aim of this review was to summarize the influence of low muscle mass (sarcopenia/cachexia) on the response and immune-related adverse events to immunotherapy from the latest literature. It was revealed that low muscle mass (sarcopenia/cachexia) has detrimental effects on cancer immunotherapy in most cases, although there were results that were not consistent with this finding. This review also discussed potential causes of the paradox, such as different measure methods, research types, muscle indicators, time point, and cancer type. Mechanically, chronic inflammation, immune cells, and microbiota may be critically involved in regulating the efficacy of immunotherapy under the condition of low muscle mass (sarcopenia/cachexia). Thus, nutritional interventions will likely be promising ways for individuals with cancer to increase the efficacy of immunotherapy in the future, for low muscle mass (sarcopenia/cachexia) is an important prognostic factor for cancer immunotherapy.

Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

BACKGROUND

Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types.

METHODS

PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted.

RESULTS

In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist.

CONCLUSIONS

There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.

Predictive impact of sarcopenia in solid cancers treated with immune checkpoint inhibitors: a meta-analysis

Sarcopenia, which is characterized by a decrease in muscle quantity or quality, is commonly observed in patients with cancer. Recent research has reported contradictory results on the association between sarcopenia and the efficacy of immune checkpoint inhibitors (ICIs). We conducted a systematic review and meta-analysis to investigate this discrepancy. We systematically searched three electronic databases to identify articles reporting on the association between sarcopenia and treatment outcomes in patients with solid cancers who received ICIs. The outcomes assessed were hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS), and odds ratios (ORs) for objective response rate (ORR), disease control rate (DCR), and toxicity. Pooled estimates and their 95% confidence intervals (CIs) were calculated. A total of 2501 patients from 26 studies were analysed. Sarcopenia was observed in 44.7% (95% CI: 38.2-51.3) of the patients and was significantly associated with poor survival (HR = 1.55, 95% CI = 1.32-1.82 for OS and HR = 1.61, 95% CI = 1.35 to 1.93 for PFS). The HRs (95% CIs) for OS according to the diagnostic measures used were 1.97 (0.88-4.41) for psoas muscle index (PMI), 1.41 (0.87-2.28) for skeletal muscle density (SMD), and 1.43 (1.23-1.67) for skeletal mass index (SMI). The HRs (95% CIs) for PFS were 1.86 (1.08-3.21) for PMI, 1.27 (0.94-1.71) for SMD, and 1.38 (1.11-1.71) for SMI. Poor radiological response to ICI therapy was observed in patients with sarcopenia (OR = 0.52, 95% CI = 0.34-0.80 for ORR and OR = 0.45, 95% CI = 0.30-0.67 for DCR). The ORs for ORR (95% CIs) were 0.56 (0.15-2.05) for PMI and 0.78 (0.56-1.09) for SMI. The oncologic outcomes associated with melanoma and non-small cell lung cancer (NSCLC) were comparable with those observed overall (HR for OS = 2.02, 95% CI = 1.26-3.24 for melanoma and HR for OS = 1.61, 95% CI = 1.19-2.18 for NSCLC). In contrast, the occurrence of severe toxicity was not associated with sarcopenia (OR = 1.13, 95% CI = 0.51-2.52). Poor survival and poor response in patients with sarcopenia indicate a negative association between sarcopenia and efficacy of ICIs. Sarcopenia's predictive ability is consistent across various tumour types. For the selection of patients who may respond to ICIs pre-therapeutically, the presence of sarcopenia should be assessed in clinical practice.

New biomarkers for checkpoint inhibitor therapy

Immune checkpoint inhibitor blockade has vastly changed treatment paradigms and improved outcomes of many solid organ malignancies. The achievements of the last decade have transformed the outcomes of several tumour types, most notably metastatic melanoma. There are, however, still large numbers of patients who receive checkpoint inhibitor therapy and do not respond. In addition to potential lack of efficacy, checkpoint inhibitors also come with a unique and sometimes devastating side-effect profile. There exists a strong need for biomarkers to accurately predict response, improve treatment selection and avoid exposing patients to toxicity where there is minimal likelihood of response. There is a wide range of methodologies investigating predictive biomarkers in this space; in this review, we address the major putative biomarkers of interest. These include conventional serum tests such as lymphocyte indices and lactate dehydrogenase, and more novel research markers such as interleukin-6 and T receptor clonality. We discuss tumorous factors that may be of interest in certain tumour types, and finally gene expression profiling. Significant research continues into many of these potential predictive biomarkers in response to the emergent need to better select patients who will benefit from treatment.