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Early preclinical evidence provided the rationale for programmed cell death 1 (PD-1) and programmed death ligand 1 (PD-L1) blockade as a potential form of cancer immunotherapy given that activation of the PD-1/PD-L1 axis putatively served as a mechanism for tumor evasion of host tumor antigen-specific T-cell immunity. Early-phase studies investigating several humanized monoclonal IgG4 antibodies targeting PD-1 and PD-L1 in advanced solid tumors paved way for the development of the first PD-1 inhibitors, nivolumab and pembrolizumab, approved by the Food and Drug Administration (FDA) in 2014. The number of FDA-approved agents of this class is rapidly enlarging with indications for treatment spanning across a spectrum of malignancies. The purpose of this review is to highlight the clinical development of PD-1 and PD-L1 inhibitors in cancer therapy to date. In particular, we focus on detailing the registration trials that have led to FDA-approved indications of anti-PD-1 and anti-PD-L1 therapies in cancer. As the number of PD-1/PD-L1 inhibitors continues to grow, predictive biomarkers, mechanisms of resistance, hyperprogressors, treatment duration and treatment beyond progression, immune-related toxicities, and clinical trial design are key concepts in need of further consideration to optimize the anticancer potential of this class of immunotherapy.

The rise of drug resistance in cancer cells presents a formidable challenge in modern oncology, necessitating the exploration of innovative therapeutic strategies. This review investigates the latest advancements in overcoming drug resistance mechanisms employed by cancer cells, focusing on emerging therapeutic modalities. The intricate molecular insights into drug resistance, including genetic mutations, efflux pumps, altered signaling pathways, and microenvironmental influences, are discussed. Furthermore, the promising avenues offered by targeted therapies, combination treatments, immunotherapies, and precision medicine approaches are highlighted. Specifically, the synergistic effects of combining traditional cytotoxic agents with molecularly targeted inhibitors to circumvent resistance pathways are examined. Additionally, the evolving landscape of immunotherapeutic interventions, including immune checkpoint inhibitors and adoptive cell therapies, is explored in terms of bolstering anti-tumor immune responses and overcoming immune evasion mechanisms. Moreover, the significance of biomarker-driven strategies for predicting and monitoring treatment responses is underscored, thereby optimizing therapeutic outcomes. For insights into the future direction of cancer treatment paradigms, the current review focused on prevailing drug resistance challenges and improving patient outcomes, through an integrative analysis of these emerging therapeutic strategies.

About 1% of non–small-cell lung cancers have ROS1 rearrangements. This oncogene is inhibited by crizotinib. In a cohort of 50 patients with ROS1 -rearranged lung cancer, crizotinib induced responses in 72%; the median duration of response was nearly a year and a half. The ROS1 oncogene encodes an orphan receptor tyrosine kinase related to anaplastic lymphoma kinase (ALK), along with members of the insulin-receptor family. 1 First discovered as the oncogene product of an avian sarcoma RNA tumor virus, 2 – 4 ROS1 (ROS1 proto-oncogene receptor tyrosine kinase) is activated by chromosomal rearrangement in a variety of human cancers, including non–small-cell lung cancer (NSCLC), cholangiocarcinoma, gastric cancer, ovarian cancer, and glioblastoma multiforme. 5 – 9 Rearrangement leads to fusion of a portion of ROS1 that includes the entire tyrosine kinase domain with 1 of 12 different partner proteins. 10 The resulting ROS1 fusion kinases are constitutively activated and drive . . .

Anaplastic lymphoma kinase (ALK) gene rearrangements are oncogenic drivers of non-small-cell lung cancer (NSCLC). Brigatinib (AP26113) is an investigational ALK inhibitor with potent preclinical activity against ALK mutants resistant to crizotinib and other ALK inhibitors. We aimed to assess brigatinib in patients with advanced malignancies, particularly ALK-rearranged NSCLC. In this ongoing, single-arm, open-label, phase 1/2 trial, we recruited patients from nine academic hospitals or cancer centres in the USA and Spain. Eligible patients were at least 18 years of age and had advanced malignancies, including ALK-rearranged NSCLC, and disease that was refractory to available therapies or for which no curative treatments existed. In the initial dose-escalation phase 1 stage of the trial, patients received oral brigatinib at total daily doses of 30–300 mg (according to a standard 3 + 3 design). The phase 1 primary endpoint was establishment of the recommended phase 2 dose. In the phase 2 expansion stage, we assessed three oral once-daily regimens: 90 mg, 180 mg, and 180 mg with a 7 day lead-in at 90 mg; one patient received 90 mg twice daily. We enrolled patients in phase 2 into five cohorts: ALK inhibitor-naive ALK-rearranged NSCLC (cohort 1), crizotinib-treated ALK-rearranged NSCLC (cohort 2), EGFRT790M-positive NSCLC and resistance to one previous EGFR tyrosine kinase inhibitor (cohort 3), other cancers with abnormalities in brigatinib targets (cohort 4), and crizotinib-naive or crizotinib-treated ALK-rearranged NSCLC with active, measurable, intracranial CNS metastases (cohort 5). The phase 2 primary endpoint was the proportion of patients with an objective response. Safety and activity of brigatinib were analysed in all patients in both phases of the trial who had received at least one dose of treatment. This trial is registered with ClinicalTrials.gov, number NCT01449461. Between Sept 20, 2011, and July 8, 2014, we enrolled 137 patients (79 [58%] with ALK-rearranged NSCLC), all of whom were treated. Dose-limiting toxicities observed during dose escalation included grade 3 increased alanine aminotransferase (240 mg daily) and grade 4 dyspnoea (300 mg daily). We initially chose a dose of 180 mg once daily as the recommended phase 2 dose; however, we also assessed two additional regimens (90 mg once daily and 180 mg once daily with a 7 day lead-in at 90 mg) in the phase 2 stage. four (100% [95% CI 40–100]) of four patients in cohort 1 had an objective response, 31 (74% [58–86]) of 42 did in cohort 2, none (of one) did in cohort 3, three (17% [4–41]) of 18 did in cohort 4, and five (83% [36–100]) of six did in cohort 5. 51 (72% [60–82]) of 71 patients with ALK-rearranged NSCLC with previous crizotinib treatment had an objective response (44 [62% (50–73)] had a confirmed objective response). All eight crizotinib-naive patients with ALK-rearranged NSCLC had a confirmed objective response (100% [63–100]). Three (50% [95% CI 12–88]) of six patients in cohort 5 had an intracranial response. The most common grade 3–4 treatment-emergent adverse events across all doses were increased lipase concentration (12 [9%] of 137), dyspnoea (eight [6%]), and hypertension (seven [5%]). Serious treatment-emergent adverse events (excluding neoplasm progression) reported in at least 5% of all patients were dyspnoea (ten [7%]), pneumonia (nine [7%]), and hypoxia (seven [5%]). 16 (12%) patients died during treatment or within 31 days of the last dose of brigatinib, including eight patients who died from neoplasm progression. Brigatinib shows promising clinical activity and has an acceptable safety profile in patients with crizotinib-treated and crizotinib-naive ALK-rearranged NSCLC. These results support its further development as a potential new treatment option for patients with advanced ALK-rearranged NSCLC. A randomised phase 2 trial in patients with crizotinib-resistant ALK-rearranged NSCLC is prospectively assessing the safety and efficacy of two regimens assessed in the phase 2 portion of this trial (90 mg once daily and 180 mg once daily with a 7 day lead-in at 90 mg). ARIAD Pharmaceuticals.

Lung cancer (LC) is a heterogeneous disease consisting mainly of two subtypes, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), and remains the leading cause of death worldwide. Despite recent advances in therapies, the overall 5-year survival rate of LC remains less than 20%. The efficacy of current therapeutic approaches is compromised by inherent or acquired drug-resistance and severe off-target effects. Therefore, the identification and development of innovative and effective therapeutic approaches are critically desired for LC. The development of RNA-mediated gene inhibition technologies was a turning point in the field of RNA biology. The critical regulatory role of different RNAs in multiple cancer pathways makes them a rich source of targets and innovative tools for developing anticancer therapies. The identification of antisense sequences, short interfering RNAs (siRNAs), microRNAs (miRNAs or miRs), anti-miRs, and mRNA-based platforms holds great promise in preclinical and early clinical evaluation against LC. In the last decade, RNA-based therapies have substantially expanded and tested in clinical trials for multiple malignancies, including LC. This article describes the current understanding of various aspects of RNA-based therapeutics, including modern platforms, modifications, and combinations with chemo-/immunotherapies that have translational potential for LC therapies.

The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells following chemotherapy is only partially understood. To understand the selection of factors driving heterogeneity before and through adaptation to treatment, we profile single-cell RNA-sequencing (scRNA-seq) transcriptomes of HGSOC tumors collected longitudinally during therapy. We analyze scRNA-seq data from two independent patient cohorts to reveal that HGSOC is driven by three archetypal phenotypes, defined as oncogenic states that describe the majority of the transcriptome variation. Using a multi-task learning approach to identify the biological tasks of each archetype, we identify metabolism and proliferation, cellular defense response, and DNA repair signaling as consistent cell states found across patients. Our analysis demonstrates a shift in favor of the metabolism and proliferation archetype versus cellular defense response archetype in cancer cells that received multiple lines of treatment. While archetypes are not consistently associated with specific whole-genome driver mutations, they are closely associated with subclonal populations at the single-cell level, indicating that subclones within a tumor often specialize in unique biological tasks. Our study reveals the core archetypes found in progressive HGSOC and shows consistent enrichment of subclones with the metabolism and proliferation archetype as resistance is acquired to multiple lines of therapy. High-grade serous ovarian cancer (HGSOC) is prone to developing resistance to treatment. Here, the authors use single-cell RNA-seq and an analysis of archetypes, and find that shifts in metabolism and proliferation are associated with the response to treatment and clonal heterogeneity in HGSOC.

Gynecological cancers include collections of cancers with diverse cellular and molecular characteristics that often develop drug resistance, making them treatment-resistant. Biomolecule-drug conjugates (BDCs), especially antibody-drug conjugates (ADCs), have revolutionized the targeted therapy of cancer; however, the creation of these entities has so far been achieved by empirical, resource-intensive design methods. The aim of this review is to critically analyze how AI can be used for the rational design and optimization of high-affinity BDCs for gynecological cancer treatment. Recent advances in machine learning (ML)- and deep learning (DL)-based methods to predict biomolecule-target binding affinity, structural compatibility, linker stability, payload selection, trafficking in the cell, and biomolecule resistance mechanisms are summarized. The review also explores the possibilities for incorporation of structural, chemical, biological, and multi-omics data to enhance specificity, efficacy, and safety of conjugates. Besides antibody-based systems, AI-assisted design approaches with peptides, aptamers, and hybrid biomolecular systems are also included. This review also highlights parameters and experimental/numerical validation restrictions related to data quality, interpretability of models, regulatory aspects, etc. AI-based conjugate engineering is increasingly moving BDC development from a largely 'trial and error' approach to a more predictive and data-driven approach. While there are still challenges to be addressed in terms of translations and validations, the potential of AI approaches in the field of precision oncology and the development of more personalized treatment is promising in the context of gynecological cancers.

Recent epidemiologic studies implying differences in cancer recurrence based on anesthetic regimens raise the possibility that the mu opioid receptor (MOR) can influence cancer progression. Based on our previous observations that overexpression of MOR in human non-small cell lung cancer (NSCLC) cells increased tumor growth and metastasis, this study examined whether MOR regulates growth factor receptor signaling and epithelial mesenchymal transition (EMT) in human NSCLC cells. We utilized specific siRNA, shRNA, chemical inhibitors and overexpression vectors in human H358 NSCLC cells that were either untreated or treated with various concentrations of DAMGO, morphine, fentanyl, EGF or IGF. Cell function assays, immunoblot and immunoprecipitation assays were then performed. Our results indicate MOR regulates opioid and growth factor-induced EGF receptor signaling (Src, Gab-1, PI3K, Akt and STAT3 activation) which is crucial for consequent human NSCLC cell proliferation and migration. In addition, human NSCLC cells treated with opioids, growth factors or MOR overexpression exhibited an increase in snail, slug and vimentin and decrease ZO-1 and claudin-1 protein levels, results consistent with an EMT phenotype. Further, these effects were reversed with silencing (shRNA) or chemical inhibition of MOR, Src, Gab-1, PI3K, Akt and STAT3 (p<0.05). Our data suggest a possible direct effect of MOR on opioid and growth factor-signaling and consequent proliferation, migration and EMT transition during lung cancer progression. Such an effect provides a plausible explanation for the epidemiologic findings.

The most difficult hurdles associated with the treatment of gynecologic cancer (such as ovarian, cervical, and endometrial cancer) is chemoresistance, with ovarian cancer representing the most clinically challenging subtype due to frequent relapse and the development of platinum resistance. Although chemotherapy remains one of the most important approaches to the management of gynecologic cancer, a large portion of patients have a poor initial response or recurrence of the treatment-resistant disease, factors that influence the rate of survival. This review highlights the present understanding of biological, molecular, and micro-environmental mechanisms that cause chemoresistance in gynecologic cancers, with primary emphasis on ovarian cancer while drawing supportive comparisons with cervical and endometrial malignancies. Important mechanisms of chemoresistance like increased drug efflux, redox based detoxification, increased capability of repairing DNA, evading apoptosis, protection by tumor microenvironment, epithelial-mesenchymal transition, persistence of cancer stem cells, epigenetic reprogramming and exosome-based communication are discussed in an integrated and easily understandable style. In addition to the overview of these mechanisms, this review also brings out newer treatment approaches that may emerge to overcome drug failure, especially in platinum-resistant ovarian cancer. These approaches include drug efflux transporter inhibitors, redox and metabolic pathways modulators, DNA repair, epigenetics, immunotherapies, tumor microenvironment, nanotechnology-delivery systems, and exosome interventions. The development in artificial intelligence and multi-omics methods that can be used to predict treatment response and deliver personalized management are also discussed, highlighting their potential role in early identification of resistance and treatment stratification in ovarian cancer patients. Collectively, these insights may help to improve therapeutic decision-making, which could enhance chemotherapy responsiveness, and support durable clinical outcomes in ovarian and other gynecologic malignancies.

Gynecological cancer, especially breast, cervical, and ovarian cancer, are significant health issues affecting women worldwide. When screened they are mostly detected at later stages because of non-specific signs and symptoms as well as the unavailability of reliable screening methods. The improvement of early oncologic prediction methods is therefore needed to work out the survival rates, guide individualized treatment, and relieve healthcare pressures. Outcome forecasting and clinical detection are rapidly changing with the use of machine learning (ML), one of the promising technologies used to analyze complex biomedical data. Artificial intelligence (AI)-based ML models are capable of determining low-level trends and making accurate predictions of disease risk and outcomes, because they can combine different datasets (clinical records, genomics, proteomics, medical imaging) and learn to identify subtle patterns. Standard algorithms, including support vector machines, random forests, and deep learning (DL) models, such as convolutional neural networks, have demonstrated high potential in identifying the type of cancer, monitoring disease progression, and designing treatment patterns. This manuscript reviews the recent developments in the use of ML models to advance oncologic prediction tasks in gynecologic oncology. It reports on critical domains, like screening, risk classification, and survival modeling, as well as comments on difficulties, like data inconsistency, inability of interpretation of models, and issues of clinical interpretation. New developments, such as explainable AI, federated learning (FL), and multi-omics fusion, are discussed to develop these models and to make them applicable in practice because of their reliability. Conclusively, this article emphasizes the transformative role of ML in precision oncology to deliver improved, patient-centered outcomes to women who are victims of gynecological cancers.