Week 2026-W22

Synthetic lethality describes a genetic interaction where two specific genetic alterations together impair cell viability, while either alteration alone is compatible with survival. This concept offers promising therapeutic avenues for targeting previously undruggable cancer pathways. High-throughput screening technologies have facilitated the discovery of novel druggable synthetic lethal vulnerabilities, particularly in DNA damage response and epigenetic alterations. Clinically approved PARP inhibitors have validated this approach in BRCA-mutant cancers. Emerging strategies targeting PRMT5, MAT2A, WRN, PKMYT1, and WEE1 are currently undergoing late preclinical or early clinical evaluation.

This article discusses the increasing complexity of therapeutic decision-making for early-stage, hormone receptor-positive, HER2-negative breast cancer. It highlights the significance of advances in tumor biology, particularly genomic assays, in improving risk stratification and identifying patients most likely to benefit from chemotherapy. The review also explores current challenges and controversies, such as the use of genomic assays in premenopausal women with node-positive disease. Furthermore, it examines the emerging role of circulating tumor DNA as a prognostic and predictive biomarker.

The FIGO staging system for endometrial cancer underwent a significant revision in 2023, incorporating refined anatomical criteria and molecular determinants. This update aims to optimize adjuvant treatment selection by maximizing therapeutic benefits while minimizing harm. Molecular subgroups include POLEmut, p53abn, MMRd, and NSMP. The 2025 ESGO-ESTRO-ESP recommendations further enhance this framework by mandating estrogen receptor (ER) assessment within the NSMP category, identifying early-stage disease with an increased risk of recurrence and mortality.

Data from the 2025 San Antonio Breast Cancer Symposium highlight the expanding role of circulating tumor DNA (ctDNA) in breast cancer management. ctDNA is now being utilized to guide therapeutic decisions, particularly in advanced hormone receptor-positive breast cancer. The SERENA-6 trial demonstrated that ctDNA-detected ESR1 mutations enabled adaptation of endocrine therapy, leading to improved clinical outcomes. Furthermore, in early-stage disease, ctDNA identifies minimal residual disease (MRD) and patients at high risk of recurrence. These advancements position ctDNA as a crucial biomarker for precision oncology and dynamic treatment adaptation.

Improved survival rates for reproductive-age breast cancer patients highlight significant oncofertility challenges. Fertility preservation, gonadotoxicity, and pregnancy safety are key concerns requiring early and integrated management within the therapeutic plan. Prompt referral for fertility preservation is crucial, ensuring it does not compromise oncologic outcomes. Modern therapies such as PARP inhibitors, CDK4/6 inhibitors, HER2-monoclonal antibodies, and endocrine treatments raise specific questions due to a lack of prospective human fertility data. A precautionary approach is recommended, including effective contraception and adherence to washout periods.

This study demonstrates that MEK1/2 inhibitors, targeting the Ras-MAPK pathway, induce AXIN1 protein loss in colorectal cancer cell lines and patient-derived organoids. Unlike GSK3 inhibitors, this loss is not attributed to altered AXIN1 protein stability or post-translational modifications, nor to a significant reduction in its transcript levels. Analyses revealed that MEK1/2 inhibitors reduce global protein synthesis via an mTOR-associated pathway, an effect sufficient to cause AXIN1 loss. Co-treatment with tankyrase inhibitors was shown to partially prevent this AXIN1 reduction.

This meta-analysis investigated the efficacy of rechallenging with CDK4/6 inhibitors (CDK4/6i) after progression in patients with HR-positive, HER2-negative advanced/metastatic breast cancer. The study included 1396 patients from eight clinical trials, comparing CDK4/6i rechallenge plus endocrine therapy to endocrine therapy alone. Results indicated a significant improvement in median progression-free survival (PFS) of 5.8 months versus 3.7 months. A greater benefit was observed when switching to a different CDK4/6 inhibitor, particularly with abemaciclib, but no overall survival benefit was demonstrated.

This study characterized the molecular architecture of the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) with somatic BRCA1/2 mutations, using single-cell sequencing and multi-omics data. BRCA1/2 mutations are linked to increased genomic instability and poor prognosis, yet predict better clinical outcomes with immune checkpoint blockade (ICB) treatment. BRCA1 mutations correlate with an upregulated type I IFN/IFN-γ signature and CD8+ T cell activation, while BRCA2 mutations are associated with inflammatory responses and enhanced MHC-II antigen presentation, influencing CD4+ T cell differentiation. The study also identified tissue-resident memory T cell (Trm) subsets as predictors of ICB response and found that a cancer-promoting program activated by BRCA1 is vulnerable to histone deacetylase inhibitors.

This study aimed to improve risk stratification for early liver metastasis in pancreatic ductal adenocarcinoma (PDAC) by integrating single-cell analysis and digital pathology. Researchers combined single-cell RNA sequencing (scRNA-seq) data from primary and metastatic PDAC tumors with transcriptomic, clinical, and H&E image data. They identified a four-gene signature (ARHGAP18, ASPH, EIF4EBP1, LY6D) associated with metastasis, which correlated with features extracted from pathological images. A pathology model based on these features demonstrated prognostic stratification capabilities in internal and external cohorts. This approach proposes a genotype-to-phenotype workflow linking scRNA-seq-derived metastatic features to routine histopathological images.

This retrospective study evaluated the efficacy and safety of neoadjuvant immunotherapy in 24 patients with locally advanced resectable melanoma. Treatment primarily involved pembrolizumab monotherapy or a combination of ipilimumab and nivolumab. A pathological complete response (pCR) rate of 16.7% and a pathological partial response (pPR) rate of 33.3% were observed. NRAS codon 61 mutations were present in 42% of patients, with 50% of these achieving a pPR. Toxicity, particularly colitis with combination therapy, represented a significant clinical challenge.

This meta-analysis assessed the efficacy and safety of single-agent versus dual immunotherapy for metastatic colorectal cancer (mCRC) with DNA mismatch repair deficiency (dMMR) or high microsatellite instability (MSI-H). PD-1/PD-L1 monotherapy significantly improved objective response rate (ORR) compared to chemotherapy, demonstrating a favorable safety profile. Dual immunotherapy (nivolumab plus ipilimumab) showed a superior ORR over monotherapy, but with increased toxicity. Benefits in progression-free survival (PFS) and overall survival (OS) were not consistently statistically significant across trials.

This retrospective multicenter Canadian study investigated treatment patterns and outcomes in 116 patients with high-risk WHO grade 2 diffuse glioma, treated with radiotherapy and adjuvant alkylating chemotherapy (temozolomide or PCV). Favorable overall survival and progression-free survival rates were observed, with 5-year rates of 94.1% and 77.9% respectively. IDH mutational status emerged as the dominant independent prognostic factor. While no significant difference in progression-free survival was found between temozolomide and PCV in IDH-mutant patients, PCV was associated with higher hematologic toxicity and more dose modifications.

The treatment paradigm for hormone receptor-positive (HR+), HER2-negative (HER2-) metastatic breast cancer (MBC) has significantly evolved from sequential therapies to more personalized strategies. CDK4/6 inhibitors combined with endocrine therapy remain the first-line standard of care, consistently improving progression-free survival and, for some agents, overall survival. The emergence of antibody-drug conjugates (ADCs) such as trastuzumab deruxtecan, sacituzumab govitecan, and datopotamab deruxtecan is reshaping the therapeutic landscape, including for HER2-low and HER2-ultralow disease. Metastasis-directed therapy is also considered for oligometastatic or oligoprogressive disease, requiring nuanced decision-making. Overall management now integrates tumor biology, therapeutic advances, and patient-defined goals of care.

A randomized, placebo-controlled phase 3 clinical trial investigated the addition of tucidinostat, a histone deacetylase inhibitor, to standard R-CHOP in patients with MYC/BCL2 double-expressor diffuse large B-cell lymphoma (DEL). The study demonstrated a significant improvement in event-free survival (EFS) in the tucidinostat group, with a 28% lower risk of events and a two-year EFS rate of 60.3% versus 50.5% for the placebo group. The complete response rate was also higher (73.0% vs 61.8%). Although increased toxicity was observed with tucidinostat, it was generally manageable.

This nationwide Japanese study resolved the status of the BRCA2 c.7847C>T (p.Ser2616Phe) variant, which is specific to the Japanese population and was previously of uncertain significance. By integrating quantitative cosegregation analyses, robust functional evidence, and population frequency data, researchers accumulated strong evidence. The evaluation led to the reclassification of this variant as "Pathogenic" according to the ClinGen ENIGMA framework. This reclassification is crucial for ensuring eligible Japanese patients gain access to appropriate targeted therapies, such as PARP inhibitors.

This paper introduces VBVarSel, a novel annealed variational Bayes algorithm designed for high-dimensional data analysis. It aims to overcome challenges associated with irrelevant variables and the selection of an appropriate number of clusters. The algorithm is particularly suited for disease subtyping and biomarker discovery in omics datasets. The authors demonstrate that VBVarSel outperforms existing methods in both simulated and real biomedical examples, specifically for cancer subtyping.

The SMC5/6 complex is crucial for genome stability, with germline variants linked to genomic instability. This pan-cancer analysis demonstrates that deleterious somatic variants in SMC5/6 genes, but not copy number alterations, are associated with an elevated tumor mutational burden (TMB). This mutagenesis is largely attributed to polymerase epsilon dysfunction and mismatch repair deficiency. Patients with these SMC5/6 variants exhibit improved survival, partly due to a superior response to immunotherapy. These findings indicate that SMC5/6 gene status could serve as a prognostic and predictive biomarker for tailored therapeutic strategies.

This multiomics study aimed to identify radioresistance biomarkers and construct a prognostic model for rectal cancer. Utilizing bulk and single-cell RNA-seq data from multiple cohorts, a risk score model based on radiotherapy-related genes was developed. This score demonstrated significant associations with immune cell infiltration and chemotherapy drug sensitivity. Five key genes potentially linked to radiotherapy sensitivity were identified and validated through protein expression and immunohistochemical analyses. These genes could serve as novel biomarkers for diagnosis, prognostic evaluation, and clinical management of rectal cancer.

Liquid biopsy represents a significant advancement in oncology, providing a minimally invasive alternative to tissue biopsy for cancer diagnosis and patient monitoring. It utilizes biomarkers such as circulating tumor DNA (ctDNA) for early cancer detection, minimal residual disease (MRD) surveillance, and dynamic treatment response monitoring. Clinical trials have demonstrated its effectiveness in earlier recurrence detection and more precise guidance for therapeutic decisions. Despite challenges related to sensitivity and technical variability, the integration of artificial intelligence and multi-omic approaches promises to enhance its future performance.

This study developed a prognostic model for colorectal cancer (CRC) using 101 machine learning algorithms based on immune-related genes. Analyzing CRC patient data, the Ridge regression model demonstrated the best performance, generating an immune-related gene risk score (IRGRS) significantly associated with lower survival rates. The IRGRS was identified as an independent prognostic factor and correlated with immune cell infiltration and stromal activity. This score could also predict response to immune checkpoint inhibitors and sensitivity to certain chemotherapies.

This study describes the development and implementation of an integrated platform designed to support decision-making within Molecular Tumor Boards (MTBs) in precision oncology. The platform utilizes an ETL (Extract, Transform, Load) process to consolidate heterogeneous clinical and molecular data, including next-generation sequencing (NGS) results. It automates data loading, significantly reducing clinicians' manual data entry time and providing an integrated view of patient status. From February 2024 to September 2025, the platform collected data from 1,119 patients and 1,221 NGS results, with therapeutic recommendations made for 30% of cases.

This study compares the performance of large language models (LLMs such as GPT-4.1-mini and Gemini-2.5-Flash) and machine learning (ML) algorithms (decision tree and XGBoost) for classifying biomedical literature according to the CIViC evidence level system. LLMs were evaluated using zero- and few-shot prompting strategies, while ML models utilized TF-IDF and word embedding representations. XGBoost with TF-IDF achieved the highest performance (micro-F1 = 0.83), outperforming both LLMs and decision trees. All models performed best on mid-range evidence levels (B to D) but struggled with high (A) and inferential (E) levels. The findings suggest that abstract-level evidence classification is largely driven by explicit lexical cues, with limited additional benefit from standalone LLM-based approaches.

This study investigates the impact of non-independent and identically distributed (Non-IID) data on the performance of Federated Learning (FL) models for survival prediction using lung cancer data. Researchers compared Random Forest (RF) and AdaBoost algorithms across various scenarios of data distribution shifts among clients. The findings indicate that both algorithms experienced significant performance degradation under highly non-IID conditions. Notably, Random Forest consistently outperformed AdaBoost in these challenging scenarios. The developed FL algorithm holds potential for model personalization and fine-tuning, with broader applicability to other clinical datasets.