To determine whether preoperative [¹⁸F]FAPI-42 PET/CT is associated with postoperative pathological phenotypes and tumor budding grade in colorectal cancer.

This retrospective dual-center study included consecutive adults who underwent [¹⁸F]FAPI-42 PET/CT before surgery between January 2022 and January 2026. The index test was quantitative primary-tumor PET uptake; the reference standard was surgical pathology including tumor budding grade. PET parameters were compared across postoperative phenotypes and binary budding groups. Logistic regression, receiver operating characteristic analysis, calibration, bootstrap validation, and decision-curve analysis were performed.

Sixty-four patients (mean age, 63.8 years ± 9.4; 40 men) with 65 primary lesions were analyzed. Maximum standardized uptake value (SUV_max) differed significantly by tumor budding grade among seven phenotypes (p = 0.012). Intermediate/high-grade budding lesions had higher SUV_max than low-grade lesions (12.66 ± 3.36 versus 10.16 ± 3.86; p = 0.012). A model combining body mass index and SUVmax achieved an area under the curve of 0.841 (95% confidence interval: 0.731, 0.934), with an optimism-corrected area under the curve of 0.830.

Preoperative [¹⁸F]FAPI-42 PET/CT uptake was associated with colorectal cancer tumor budding. A simple body mass index-SUV_max model provides internally validated, interpretable preoperative information on tumor budding grade; however, independent multicenter external validation is required before clinical application.

Question Can preoperative fibroblast activation protein inhibitor PET/CT identify colorectal cancer tumor budding, a clinically relevant marker of aggressive invasive-front biology? Findings Higher [¹⁸F]FAPI-42 uptake was associated with intermediate/high-grade tumor budding; combining body mass index and maximum standardized uptake value improved discrimination. Clinical relevance Fibroblast activation protein inhibitor PET/CT may support noninvasive preoperative risk stratification of colorectal cancer by identifying tumor budding-related aggressive biology without additional procedures.

Average intensity projection (AIP) is an image generation method obtained by averaging the images of each phase of 4D-CT. This study aimed to evaluate tumor respiratory motion and, using the union of contours from all 4D-CT phases (4D_Total) as the reference, to compare contour agreement among contours derived from 3D-CT, maximum intensity projection (MIP), and AIP images. In addition, dose metrics in treatment planning were evaluated to explore the dosimetric impact of contour differences among the image sets.

Eight patients who underwent lung stereotactic body radiotherapy (SBRT) at our institution were included. Tumor motion was first characterized from centroid positions across ten 4D-CT phases. Next, 4D_Total was generated by taking the union of the contours extracted from each phase. Finally, contours from 4D-CT AIP, 4D-CT MIP, and slow-scan 3D-CT (3D_Slow) were compared with 4D_Total using volume difference, centroid difference, Dice similarity coefficient (DSC), Jaccard index, 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and surface Dice similarity coefficient with a 2-mm tolerance. In addition, treatment plans were generated using each contour set, and Dmax (cGy), lung V5 (%), lung V20 (%), conformity index (CI), and homogeneity index (HI) were compared.

Tumor motion was greatest in the inferior-superior direction and was generally multi-directional. For AIP, centroid difference was ≤1 mm in each direction (LR/AP/IS), and the median values of DSC, Jaccard index, HD95, MSD, and surface DSC (2 mm) were 0.831, 0.711, 1.707 mm, 0.451 mm, and 0.981, respectively, showing good geometric agreement with 4D_Total. No significant differences were observed between AIP and MIP, whereas both AIP and MIP showed better agreement than 3D_Slow. In the dose evaluation, no significant overall differences were observed for any metric among the four contour sets. In the exploratory analysis, paired comparisons using 4D_Total as the reference showed a directional decrease in lung V20 for AIP and a directional increase in CI for MIP. These findings should not be interpreted as definitive conclusions, but rather as results of an exploratory analysis based on a limited number of cases.

The AIP method showed good geometric approximation to 4D_Total and may represent the time-dependent displacement of tumor position within a single dataset. In this limited exploratory analysis, no major disadvantage in treatment planning was observed. However, the observed changes in dose metrics, including lung V20, should not be interpreted as evidence of dosimetric superiority and require further validation. Because of uncertainty in the reference contour and the limited number of cases, further investigation is needed to clarify its clinical significance.

Tumors exhibit the Warburg effect, characterized by elevated glucose metabolism through both oxidative phosphorylation and glycolysis to produce energy. To maintain intracellular pH and metabolic homeostasis, cancer cells depend on monocarboxylate transporters (MCTs) to export the glycolytic end product, l-lactate. Similar to other cancers, glucose metabolism is enhanced in T-cell acute lymphoblastic leukemia (T-ALL), supporting rapid cell proliferation. Although MCT1 expression and its sensitivity to inhibition have been reported in certain T-ALL models, the relative contributions of other co-expressed MCT isoforms (MCT2 and MCT4) to l-lactate transport are unclear. This study aimed to evaluate the functional contribution of MCT isoforms to l-lactate transport in human T-ALL cell lines. We examined the expression and function of MCTs in Jurkat E6-1 and MOLT-3 cells. RT-PCR and Western blot analyses confirmed mRNA and protein expression of MCT1, MCT2, and MCT4 in both cell lines. Treatment with AZD3965, an MCT1 and MCT2 inhibitor, significantly increased intracellular l-lactate levels in T-ALL cells. In contrast, selective inhibition of MCT2 with atorvastatin or MCT4 with AZD0095 and VB124 did not alter intracellular l-lactate levels. Consistent with these pharmacological findings, MCT1 knockdown using small interfering RNA led to significant intracellular l-lactate accumulation. Collectively, these findings indicate that MCT1 is the principal transporter responsible for l-lactate transport in T-ALL cell lines, despite the co-expression of MCT2 and MCT4. Of all the multiple co-expressed isoforms, MCT1 is the primary mediator of l-lactate transport and is a promising therapeutic target for T-ALL treatment.

To evaluate machine learning models for predicting high-grade squamous intra-epithelial lesions and cervical intra-epithelial neoplasia grade 2 or higher using sociodemographic and clinical parameters.

A retrospective cohort study was conducted using data from the Subred Norte (Bogotá, Colombia), a public health network, between 2018 and 2024. Records lacking sociodemographic data were excluded. To address the 10% class imbalance, random under-sampling was applied to train balanced models, which were then compared with the original imbalanced models.

The final sample included 4367 patients (aged 21 to 65 years) with cytology, colposcopy, or biopsy reports. The real-world prevalence of histopathologically confirmed cervical intra-epithelial neoplasia grade 2 or higher in this cohort was 10%. Five machine learning classification methods were tested across 2 datasets: 2018-2022 (n = 3139, excluding human papillomavirus history due to under-reporting) and 2023-2024 (n = 1228, including human papillomavirus history). On the original imbalanced dataset (without human papillomavirus history), the random forest model achieved an area under the receiver operating characteristic curve of 0.91 and a specificity of 99%, but had a sensitivity of only 41%. After applying random under-sampling, the random forest model's sensitivity improved to 89%, with a precision of 84%. For the dataset including human papillomavirus history, the support vector machine model achieved the highest balanced performance, yielding similar metrics. However, because these models were developed and evaluated strictly within a single institution, these balanced metrics represent optimistic internal validation performance.

Machine learning models show promising performance for predicting cervical intra-epithelial neoplasia grade 2 or higher, with improved sensitivity after addressing class imbalance. These findings support further external, multi-center validation before clinical implementation.

Breast cancer (BC) remains the most prevalent malignancy among women and a major contributor to global cancer mortality. Unfortunately, early detection, therapeutic targeting, and prognosis remains challenging due to the profound molecular and clinical heterogeneity in BC. However, recent advances in multi-omics technologies have transformed biomarker discovery by enabling comprehensive interrogation of the genomics, transcriptomics, proteomics, and metabolic networks that drive BC pathogenesis. This chapter reviews key developments in biomarker research and the technological platforms that support their discovery and validation. Innovations in mass spectrometry, single-cell and spatial proteomics, and nanoproteomics have expanded the detectable proteome and markedly improved analytical sensitivity, particularly for low-abundance and heterogeneous tumor signals. Complementary array- and aptamer-based assays further enhance high-throughput, quantitative, and minimally invasive profiling, providing scalable routes toward clinical implementation across diverse BC subtypes. Beyond experimental technologies, the chapter examines emerging computational frameworks that integrate multi-omics data using artificial intelligence and machine learning. These approaches enable multidimensional data interpretation and facilitate the identification of dynamic molecular signatures associated with disease progression, therapeutic response, and resistance. By outlining current methodologies, translational challenges, and future directions, this chapter highlights how converging technological and analytical advances are reshaping the BC biomarker landscape and paving the way toward more precise, adaptive, and clinically actionable diagnostic and therapeutic strategies.

Interleukin-7 receptor (IL7R) signaling is critical for T-cell survival, homeostasis, and immune function, but the therapeutic potential of increasing IL7R expression through messenger RNA (mRNA) delivery remains poorly defined. We investigated whether IL7R mRNA therapy could enhance antitumor immunity and improve tumor control in preclinical cancer models.

IL7R mRNA formulated in lipid nanoparticles (LNPs) was administered by combined intratumoral and intravenous injection in syngeneic B16 melanoma and Lewis lung carcinoma (LLC) models, with empty LNPs included as vehicle controls. Therapeutic efficacy, IL7R expression, immune responses, transcriptomic remodeling, T-cell receptor (TCR) repertoire changes, and treatment tolerability were evaluated using complementary molecular, immunological, and sequencing approaches.

IL7R mRNA treatment increased IL7R transcript and protein levels in tumor-bearing mice, whereas empty LNPs did not produce comparable increases. Treatment increased tumor-infiltrating CD8 T-cell frequencies, suppressed tumor growth and prolonged survival in both B16 and LLC models; empty LNP controls did not reproduce the therapeutic effect. Cytokine and transcriptomic analyses showed treatment-associated activation of inflammatory, T-cell, interferon, and antigen-receptor signaling programs. TCR sequencing further demonstrated increased repertoire diversity and reduced clonal dominance following IL7R mRNA treatment. No obvious body-weight loss or marked changes in serum alanine aminotransferase, aspartate aminotransferase, or creatinine were observed under the tested conditions.

IL7R mRNA therapy increases IL7R expression and is associated with enhanced T-cell antitumor responses, tumor growth suppression, improved survival, and remodeling of the tumor immune environment. These findings support further mechanistic and preclinical evaluation of IL7R mRNA-based cancer immunotherapy.

CD6 is a promising target in cancer immunotherapy, with preclinical evidence supporting its role in modulating immune responses. This warrants further investigation into current CD6-targeting treatments, which could lead to new anti-tumor therapies. However, the clinical effects of targeting CD6 in solid tumors remain unexplored.

This proof-of-concept study is the first in-human evaluation of the safety and early effectiveness of anti-CD6 monoclonal antibody itolizumab in patients with advanced, highly heterogeneous solid tumors. Treatment had two phases: induction and maintenance. In induction, group A received 100 mg weekly for nine doses; group B received 200 mg biweekly for five doses. After completing induction, patients moved to maintenance 21 days later, receiving 100 mg every 21 days for up to six doses. The primary endpoint was safety; secondary objectives included disease control, survival, and exploratory molecular mechanisms and blood biomarkers.

Itolizumab demonstrated a favorable safety profile, with no safety-related treatment discontinuations, no related serious adverse events, and no unexpected toxicities. In this heavily treated, non-biomarker-selected population, disease control was achieved in 25% in group A and 20% in group B, and the 1-year survival rate was 26% in group A and 15% in group B. Mechanistically, ex vivo analysis demonstrated that itolizumab downregulates CD6, increasing soluble CD6 levels and promoting a cytotoxic phenotype on CD8 and NK cells. Additionally, patient-derived peripheral blood mononuclear cells demonstrated boosted in vitro killing capacity specifically against CD318+ cancer cells. The antibody also modulates PD-1 expression in peripheral lymphocytes, promoting NK cell activity while maintaining inhibitory signals on CD8+ T cells and decreasing T helper overactivation. These findings support the dual role of itolizumab, enhancing anti-tumor responses while preventing autoimmune-like effects by reducing off-target inflammation and avoiding immune-related adverse effects associated with immune checkpoint therapies.

Overall, these findings support further development of itolizumab as a rational immunomodulatory strategy with a favorable safety profile for advanced solid tumors, particularly in CD318-expressing tumors and as part of rational combination regimens.

RPCEC00000444.

Identifying robust predictive biomarkers for immune checkpoint blockade (ICB) remains a challenge. While alternative splicing (AS) shapes tumor immunity, the lack of large-scale, consensus-based datasets constrains the characterization of ICB-specific splicing landscapes.

We developed TCASIA (the Cancer Alternative Splicing Immunotherapy Atlas), harmonizing >4000 samples across 29 cohorts. A rigorous "2-out-of-4" multi-algorithm consensus framework (rMATS, MAJIQ, SUPPA2, and SplAdder) with a fuzzy matching strategy was used to establish a high-confidence AS landscape.

TCASIA provides a robust toolkit linking splicing perturbations with clinical phenotypes (response, survival, and immune infiltration). Using this platform, we recapitulated the established FAS exon 6 skipping mechanism and identified IL7R exon 6 skipping as a functionally supported candidate associated with reduced CD8⁺ T-cell infiltration and poor survival in melanoma.

TCASIA provides an analytical atlas for investigating splicing-mediated immune evasion and prioritizing isoform-specific candidates for further functional and clinical validation in the context of ICB treatment.

Lhermitte's sign (LS) is characterised by a brief electric shock-like sensation elicited by neck flexion that radiates down the spine and may extend into the posterior aspects of the legs. It is a clinical indicator of cervical or upper thoracic dorsal column dysfunction, most commonly due to demyelination or intrinsic spinal cord pathology. It may occur after head and neck (chemo)radiotherapy. We describe two patients with human papillomavirus-associated oropharyngeal squamous cell carcinoma who developed LS following chemoradiotherapy. One patient developed protracted LS lasting 20 months beyond completion of definitive concurrent chemoradiotherapy with volumetric modulated arc radiotherapy and weekly cisplatin and another patient developed protracted LS after adjuvant de-escalated volumetric modulated arc radiotherapy lasting 12 months beyond treatment completion. These cases show the variable course of LS in the modern conformal era and highlight its benign prognosis but potential for persistent symptoms that affect patient quality of life.

Oral anticancer agent (OAA) use in cancer treatment necessitates patient symptom self-management, which requires self-efficacy. Appropriate management may deter costly and burdensome unplanned healthcare use.

To assess self-efficacy for managing symptoms over time in patients taking targeted OAAs, and to evaluate associations between self-efficacy, symptom severity, and unplanned healthcare use.

We conducted a prospective cohort study of participants initiating OAAs, following them for approximately the first eight months of therapy. Participants completed patient reported outcome measures (PROMs) online at six timepoints. Descriptive statistics quantified demographics, cancer characteristics, symptoms, self-efficacy for managing symptoms, and unplanned healthcare use. Logistic regression models estimated whether unplanned healthcare service use was associated with self-efficacy for managing symptoms and symptom severity. Mixed effects logistic regressions were used to account for repeated measures and time effects.

Fifty-nine participants (95%) completed the first set of PROMs within three weeks of starting therapy; about eight months later, 29 (47%) completed the sixth and final set. Participants were mostly White (90%) and female (61%). Twenty-four unique participants (41%) used unplanned healthcare 38 times. Multivariable adjusted odds of unplanned healthcare use were lower for participants with above versus below average self-efficacy (OR 0.24; 95% CI 0.11-0.53), but higher with each one-point increase in pain severity (OR 1.29; 95% CI 1.11-1.51).

Unplanned healthcare use was associated with lower self-efficacy for managing symptoms and greater pain severity. Clinicians should redouble efforts to assess and manage pain proactively. Assessing self-efficacy to deliver targeted support to patients may potentially reduce unplanned healthcare use.