Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

Standard first-line chemotherapy results in disease progression and death within one year in most patients with human epidermal growth factor receptor 2 (HER2)-negative gastro-oesophageal adenocarcinoma^1–4. Nivolumab plus chemotherapy demonstrated superior overall survival versus chemotherapy at 12-month follow-up in gastric, gastro-oesophageal junction or oesophageal adenocarcinoma in the randomized, global CheckMate 649 phase 3 trial⁵(programmed death ligand-1 (PD-L1) combined positive score ≥5 and all randomized patients). On the basis of these results, nivolumab plus chemotherapy is now approved as a first-line treatment for these patients in many countries⁶. Nivolumab and the cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitor ipilimumab have distinct but complementary mechanisms of action that contribute to the restoration of anti-tumour T-cell function and induction of de novo anti-tumour T-cell responses, respectively^7–11. Treatment combining 1 mg kg⁻¹nivolumab with 3 mg kg⁻¹ipilimumab demonstrated clinically meaningful anti-tumour activity with a manageable safety profile in heavily pre-treated patients with advanced gastro-oesophageal cancer¹². Here we report both long-term follow-up results comparing nivolumab plus chemotherapy versus chemotherapy alone and the first results comparing nivolumab plus ipilimumab versus chemotherapy alone from CheckMate 649. After the 24.0-month minimum follow-up, nivolumab plus chemotherapy continued to demonstrate improvement in overall survival versus chemotherapy alone in patients with PD-L1 combined positive score ≥5 (hazard ratio 0.70; 95% confidence interval 0.61, 0.81) and all randomized patients (hazard ratio 0.79; 95% confidence interval 0.71, 0.88). Overall survival in patients with PD-L1 combined positive score ≥ 5 for nivolumab plus ipilimumab versus chemotherapy alone did not meet the prespecified boundary for significance. No new safety signals were identified. Our results support the continued use of nivolumab plus chemotherapy as standard first-line treatment for advanced gastro-oesophageal adenocarcinoma.

Pancreatic ductal adenocarcinoma: Treatment hurdles, tumor microenvironment and immunotherapy

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported. We report 3-year efficacy and safety results from the phase III CheckMate 649 trial. Patients with previously untreated advanced or metastatic gastroesophageal adenocarcinoma were randomly assigned to nivolumab plus chemotherapy or chemotherapy. Primary end points were overall survival (OS) and progression-free survival (PFS) by blinded independent central review (BICR) in patients whose tumors expressed PD-L1 combined positive score (CPS) ≥5. With 36.2-month minimum follow-up, for patients with PD-L1 CPS ≥5, the OS hazard ratio (HR) for nivolumab plus chemotherapy versus chemotherapy was 0.70 (95% CI, 0.61 to 0.81); 21% versus 10% of patients were alive at 36 months, respectively; the PFS HR was 0.70 (95% CI, 0.60 to 0.81); 36-month PFS rates were 13% versus 8%, respectively. The objective response rate (ORR) per BICR was 60% (95% CI, 55 to 65) with nivolumab plus chemotherapy versus 45% (95% CI, 40 to 50) with chemotherapy; median duration of response was 9.6 months (95% CI, 8.2 to 12.4) versus 7.0 months (95% CI, 5.6 to 7.9), respectively. Nivolumab plus chemotherapy also continued to show improvement in OS, PFS, and ORR versus chemotherapy in the overall population. Adding nivolumab to chemotherapy maintained clinically meaningful long-term survival benefit versus chemotherapy alone, with an acceptable safety profile, supporting the continued use of nivolumab plus chemotherapy as standard first-line treatment for advanced gastroesophageal adenocarcinoma.

The Androgen Receptor (AR) is emerging as an important factor in the pathogenesis of breast cancer (BC), which is the most common malignancy among females worldwide. The concordance of more than 70% of AR expression in primary and metastatic breast tumors implies that AR may be a new marker and a potential therapeutic target among AR-positive breast cancer patients. Biological insight into AR-positive breast cancer reveals that AR may cross-talk with several vital signaling pathways, including key molecules and receptors. AR exhibits different behavior depending on the breast cancer subtype. Preliminary clinical research using AR-targeted drugs, which have already been FDA-approved for prostate cancer (PC), has given promising results for AR-positive breast cancer patients. However, since the prognostic and predictive value of AR positivity remains uncertain, it is difficult to identify and stratify patients that would benefit from AR-targeted therapies. Herein, through a review of preclinical studies, clinical studies, and clinical trials, we summarize the biology of AR, its prognostic and predictive value, as well as its therapeutic implications by breast cancer molecular subtype.

Cachexia is a major characteristic of multiple non-malignant diseases, advanced and metastatic cancers and it is highly prevalent in pancreatic cancer, affecting almost 70%–80% of the patients. Cancer cachexia is a multifactorial condition accompanied by compromised appetite and changes in body composition, i.e., loss of fat. It is associated with lower effectiveness of treatment, compromised quality of life, and higher mortality. Understanding the complex pathways underlying the pathophysiology of cancer cachexia, new therapeutic targets will be unraveled. The interplay between tumor and host factors, such as cytokines, holds a central role in cachexia pathophysiology. Cytokines are possibly responsible for anorexia, hypermetabolism, muscle proteolysis, and apoptosis. In particular, cachexia in pancreatic cancer might be the result of the surgical removal of pancreas parts. In recent years, many studies have been carried out to identify an effective treatment algorithm for cachexia. Choosing the most appropriate treatment, the clinical effect and the risk of adverse effects should be taken under consideration. The purpose of this review is to highlight the pathophysiological mechanisms as well as the current ways of cachexia treatment in the pharmaceutical and the nutrition field.