An immune-based risk-stratification system for predicting prognosis in pulmonary sarcomatoid carcinoma (PSC)

Clinical characteristics and survival outcomes in patients with pulmonary sarcomatoid carcinoma: a multicenter retrospective study

PURPOSE

The clinicopathologic features, mutational status, immunohistochemical markers, and prognosis of Pulmonary sarcomatoid carcinoma (PSC) remain uncertain.

METHODS

This study included 81 PSC and 337 lung adenocarcinomas (LUAD). Progression-free survival (PFS), overall survival (OS), and other clinical data were examined.

RESULTS

46% PSC patients harbored KRAS mutation and 23% harbored EGFR mutation. Univariable analysis identified type and cTNM stage as significant predictor of PFS (type: HR 0.216; 95% CI 0.133-0.349; P < 0.001, cTNM stage: HR 0.483; 95% CI 0.269-0.846; P = 0.014) and OS (type: HR 0.269; 95% CI 0.156-0.465; P < 0.001, cTNM stage: HR 0.435; 95% CI 0.219-0.865; P = 0.018). Multivariable analysis confirmed sex, type and cTNM stage as independent predictors of PFS (sex: HR 2.026; 95%CI 1.027-3.996; P = 0.042; type: HR0.140; 95% CI 0.083-0.238; P < 0.001, cTNM stage: HR0.305; 95% CI 0.165-0.564; P < 0.001) and OS (type: HR0.231; 95% CI 0.132-0.404; P < 0.001, cTNM stage: HR 0.394; 95% CI 0.194-0.797; P = 0.010). Significant differences in PFS (P < 0.0001) and OS (P = 0.022) were observed between PSC and LUAD, and for PC compared with SCC (PFS: P = 0.00036, OS: P = 0.0053). Additionally, PSC patients treated with immunotherapy showed significantly better OS (P = 0.0019) compared with those treated without immunotherapy.

CONCLUSIONS

PSC exhibits high KRAS and EGFR mutation rates, and spindle cell carcinoma has a worse prognosis. Immunotherapy shows potential as a treatment for advanced PSC.

Blood Microbiota and Its Products: Mechanisms of Interference with Host Cells and Clinical Outcomes

In healthy conditions, blood was considered a sterile environment until the development of new analytical approaches that allowed for the detection of circulating bacterial ribosomal DNA. Currently, debate exists on the origin of the blood microbiota. According to advanced research using dark field microscopy, fluorescent in situ hybridization, flow cytometry, and electron microscopy, so-called microbiota have been detected in the blood. Conversely, others have reported no evidence of a common blood microbiota. Then, it was hypothesized that blood microbiota may derive from distant sites, e.g., the gut or external contamination of blood samples. Alteration of the blood microbiota's equilibrium may lead to dysbiosis and, in certain cases, disease. Cardiovascular, respiratory, hepatic, kidney, neoplastic, and immune diseases have been associated with the presence of Gram-positive and Gram-negative bacteria and/or their products in the blood. For instance, lipopolysaccharides (LPSs) and endotoxins may contribute to tissue damage, fueling chronic inflammation. Blood bacteria can interact with immune cells, especially with monocytes that engulf microorganisms and T lymphocytes via spontaneous binding to their membranes. Moreover, LPSs, extracellular vesicles, and outer membrane vesicles interact with red blood cells and immune cells, reaching distant organs. This review aims to describe the composition of blood microbiota in healthy individuals and those with disease conditions. Furthermore, special emphasis is placed on the interaction of blood microbiota with host cells to better understand disease mechanisms.

Integrative genomic and transcriptomic profiling of pulmonary sarcomatoid carcinoma identifies molecular subtypes associated with distinct immune features and clinical outcomes

Background

Pulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive subtype of non-small cell lung cancer (NSCLC), characterized by the presence of epithelial and sarcoma-like components. The molecular and immune landscape of PSC has not been well defined.

Methods

Multiomics profiling of 21 pairs of PSCs with matched normal lung tissues was performed through targeted high-depth DNA panel, whole-exome, and RNA sequencing. We describe molecular and immune features that define subgroups of PSC with disparate genomic and immunogenic features as well as distinct clinical outcomes.

Results

In total, 27 canonical cancer gene mutations were identified, with TP53 the most frequently mutated gene, followed by KRAS. Interestingly, most TP53 and KRAS mutations were earlier genomic events mapped to the trunks of the tumors, suggesting branching evolution in most PSC tumors. We identified two distinct molecular subtypes of PSC, driven primarily by immune infiltration and signaling. The Immune High (IM-H) subtype was associated with superior survival, highlighting the impact of immune infiltration on the biological and clinical features of localized PSCs.

Conclusions

We provided detailed insight into the mutational landscape of PSC and identified two molecular subtypes associated with prognosis. IM-H tumors were associated with favorable recurrence-free survival and overall survival, highlighting the importance of tumor immune infiltration in the biological and clinical features of PSCs.

Prognostic Value of 18F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma

OBJECTIVE

To investigate the prognostic utility of radiomics features extracted from 18F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL).

MATERIALS AND METHODS

A total of 126 adults with ENKTCL who underwent 18F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve.

RESULTS

Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance.

CONCLUSION

The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

Galectin-9 in cancer therapy: from immune checkpoint ligand to promising therapeutic target

Galectin-9 (Gal-9) is a vital member of the galectin family, functioning as a multi-subtype galactose lectin with diverse biological roles. Recent research has revealed that Gal-9's interaction with tumors is an independent factor that influences tumor progression. Furthermore, Gal-9 in the immune microenvironment cross-talks with tumor-associated immune cells, informing the clarification of Gal-9's identity as an immune checkpoint. A thorough investigation into Gal-9's role in various cancer types and its interaction with the immune microenvironment could yield novel strategies for subsequent targeted immunotherapy. This review focuses on the latest advances in understanding the direct and indirect cross-talk between Gal-9 and hematologic malignancies, in addition to solid tumors. In addition, we discuss the prospects of Gal-9 in tumor immunotherapy, including its cross-talk with the ligand TIM-3 and its potential in immune-combination therapy.

Integrated pathological analysis to develop a Gal-9 based immune survival stratification to predict the outcome of lung large cell neuroendocrine carcinoma and its usefulness in immunotherapy

This study aimed to integrate the cell spatial organization to develop a Gal-9-based immune survival stratification in the lung large cell neuroendocrine carcinoma (LCNEC) and investigate its potentials to immunotherapy. The expression of Gal-9 and other twelve immune markers were evaluated in 122 cases of surgical LCNEC samples from our center using immunohistochemistry. The Gal-9-based immune survival stratification risk score was constructed and its predictive performance was evaluated. Then, we thoroughly explored the effects of Gal-9 and immune risk score on LCNEC immune pathways, immune micro-environment and immunotherapy sensitivity in different cohort and platform, and made a validation in pathology images using Histology-based Digital-Staining (HDS). In 122 LCNEC samples, 43 cases were positive Gal-9 expression on tumor cells (Gal-9 TC). Increased Gal-9 TC predicted worse overall survival. Gal-9's interaction with other immune markers added to the immune suppression and immune tolerance in LCNEC. Immune protein marker-based risk score consisting of Gal-9, CD3, CD4, PD-L1, and PD-1 was developed and validated to robustly discriminate survival high-risk or low-risk in LCNEC patients. The high-risk group characterized by immune-desert tumor had less various T cells. The low-risk group featuring immune-inflamed tumor was more likely to respond to anti-PD1 immunotherapy. HDS in 122 LCNEC samples' 108,369 cells validated that the high-risk group had more tumor cells, less stromal cells, less lymphocytes, higher tumor cell nucleic solidity and lower stromal cells nucleic solidity. An integrated pathological analysis confirms the Gal-9 based immune survival stratification is distinctively related to micro-environment status involved in immune suppression and immune tolerance and could act as a combinatorial biomarker to predict the outcome of LCNEC. These findings may help effectively stratify LCNEC patients sensitive to immunotherapy.

Applications of machine learning in tumor-associated macrophages

Evaluation of tumor-host interaction and intratumoral heterogeneity in the tumor microenvironment (TME) is gaining increasing attention in modern cancer therapies because it can reveal unique information about the tumor status. As tumor-associated macrophages (TAMs) are the major immune cells infiltrating in TME, a better understanding of TAMs could help us further elucidate the cellular and molecular mechanisms responsible for cancer development. However, the high-dimensional and heterogeneous data in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data sets. With the application of machine learning in analyzing TME, especially TAM’s traceable status, we could better understand the role of TAMs in tumor biology. Furthermore, we envision that the promotion of machine learning in this field could revolutionize tumor diagnosis, treatment stratification, and survival predictions in cancer research. In this article, we described key terms and concepts of machine learning, reviewed the applications of common methods in TAMs, and highlighted the challenges and future direction for TAMs in machine learning.

Treatment of Multiple Primary Malignancies With PD-1 Inhibitor Camrelizumab: A Case Report and Brief Literature Review

Background

With significant advances in the diagnostic tools and treatment modalities of cancer, the incidence of multiple primary malignancies (MPMs) has increased in the last decades. The therapeutic option changed with the arising of immune checkpoint inhibitors (ICIs), which have improved the survival of a broad spectrum of tumors. However, little information is available when it comes to the efficacy, resistance, and underlying mechanisms of ICIs.

Case Presentation

A 67-year-old woman was diagnosed with pulmonary sarcomatoid carcinoma (PSC) with a history of hepatocellular carcinoma (HCC) and viral hepatitis B. Following the lack of response to systemic chemotherapy, she was treated with camrelizumab, an anti-programmed cell death protein 1 monoclonal antibody, in combination with chemotherapy, and a partial response was obtained both in PSC and HCC. After a course of 9-month treatment, the PSC lesion shrank still, while HCC was evaluated as a progressive disease with an increase in the diameter of liver neoplasm, elevated alpha-fetoprotein, and enlarged abdominal lymph nodes. Then, with the addition of radiotherapy for abdominal metastasis, the lung lesion was continuously shrinking. In the meantime, the liver neoplasm and abdominal lymph nodes showed no significant enlargement.

Conclusion

Camrelizumab combination therapy could consistently benefit the MPM patients with PSC and HCC, which may be a promising option for patients with MPMs.