Immune cell engagers in solid tumors: promises and challenges of the next generation immunotherapy

In the landscape of cancer immunotherapy, immune cell engagers (ICEs) are rapidly emerging as a feasible and easy-to-deliver alternative to adoptive cell therapy for the antitumor redirection of immune effector cells. Even if in hematological malignancies this class of new therapeutics already hit the clinic, the development of ICEs in solid tumors still represents a challenge. Considering that ICEs are a rapidly expanding biotechnology in cancer therapy, we designed this review as a primer for clinicians, focusing on the major obstacles for the clinical implementation and the most translatable approaches proposed to overcome the limitations in solid tumors.

Toxicity assessment on trophoblast cells for some environment polluting chemicals and 17β-estradiol

The identification of reproductive toxicants is a major scientific challenge for human health. We investigated the effects of a selected group of environmental polluting chemicals mostly provided with estrogenic activity on the human trophoblast cell lines BeWo and HTR-8/SVneo. Cells were exposed for 24h to various concentrations (from 0.1 pM to 1 mM) of atrazine (ATR), diethylstilbestrol (DES), para-nonylphenol (p-NP), resveratrol (RES) and 17 β-estradiol (E2) and assayed for cell viability and human beta-Chorionic Gonadotropin (β-hCG) secretion. Decrease of cell viability as respect to control, vehicle-treated, cultures was obtained for all chemicals in the concentration range of 1 μM-1 mM in both cell types. A parallel decrease of β-hCG secretion was observed in BeWo cells, at 1 μM-1 mM concentrations, with the only exception of ATR which caused an increase at concentrations up to 1mM. β-hCG release was also unexpectedly inhibited by ATR, DES, p-NP and RES at non-toxic (pM-nM) concentrations. These findings raise concern about the negative, potential effects of various environmental polluting chemicals on pregnancy success and fetal health.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Cancer-related fatigue and depression: a monocentric, prospective, cross-sectional study in advanced solid tumors

Background

Cancer-related fatigue (CRF) is common in patients with advanced solid tumors and several risk factors are described. The possible role of depression is reported by clinicians despite the association with CRF being unclear.

Material and methods

In this monocentric, cross-sectional, prospective study we recruited patients with advanced solid tumors who were hospitalized at Fondazione IRCCS Istituto Nazionale dei Tumori of Milan. The primary objective was to assess the correlation between CRF and depression. Secondary objectives were the estimation of CRF and depression prevalence and the identification of associated clinical risk factors. CRF and depression were evaluated through the Functional Assessment of Cancer Therapy-Fatigue subscale and the Zung Self Depression Scale (ZSDS) questionnaires. The Cochran-Armitage trend test was used to demonstrate the primary hypothesis. Univariate and multivariate logistic regression models were used to investigate the impact of clinical variables.

Results

A total of 136 patients were enrolled. The primary analysis found a linear correlation (P < 0.0001) between CRF and depression. The prevalence of CRF and of moderate to severe depressive symptoms was 43.5% and 29.2%, respectively. In univariate analysis, patients with poor Eastern Cooperative Oncology Group performance status (ECOG PS), anemia, distress, pain, and receiving oncological treatment were at a significantly higher risk for CRF, whereas poor ECOG PS, pain, and distress were risk factors for depression. In multivariate analysis, high levels of ZSDS were confirmed to be correlated to CRF: odds ratio of 3.86 [95% confidence interval (CI) 0.98-15.20) and 11.20 (95% CI 2.35-53.36) for ZSDS of 50-59 and 60-100, respectively (P value for trend 0.002). Moreover, the ECOG PS score was confirmed to be significantly associated with CRF (OR 7.20; 95% CI 1.73-29.96; P = 0.007).

Conclusions

Our data suggest a strong correlation between CRF and depression in patients with advanced solid tumors. Further investigations are needed to better understand this relationship and if depressive disorder therapeutic strategies could also impact on CRF.

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Validated patient-reported outcome measures were used for screening CRF and depression in advanced cancer patients.

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A direct strong correlation between CRF and depression was found in these patients, often unconsidered by clinicians.

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Other different clinical risk factors for the onset and worsening of CRF were identified.

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A comprehensive evaluation of cancer patients, that should also consider mood disorders, could improve CRF management.