Cancer letters最新文献

Significance

Resistance to neoadjuvant chemoradiation therapy (neo-CRT) is a significant clinical problem in the treatment of locally advanced rectal cancer. Identification of novel therapeutic targets and biomarkers predicting therapeutic response is required to improve patient outcomes. Increasing evidence supports a role for the complement system in resistance to anti-cancer therapy. In this study, increased expression of complement effectors C3 and C5 and increased production of anaphylatoxins, C3a and C5a, was observed in radioresistant rectal cancer cells. Modulation of the central complement effector, C3, was demonstrated to functionally alter the radioresponse, with C3 overexpression significantly enhancing radioresistance, whilst C3 inhibition significantly increased sensitivity to a clinically-relevant dose of radiation. Inhibition of C3 was demonstrated to increase DNA damage and alter cell cycle distribution, mediating a shift towards a radiosensitive cell cycle phenotype suggesting a role for C3 in reprogramming of the tumoural radioresponse. Expression of the complement effectors C3 and C5 was significantly increased in human rectal tumour tissue, as was expression of CFB, a component of the alternative pathway of activation. Elevated levels of C3a and C5b-9 in pre-treatment sera from rectal cancer patients was associated with subsequent poor responses to neo-CRT and poorer survival. Together these data demonstrate a role for complement in the radioresistance of rectal cancer and identify key complement components as potential biomarkers predicting response to neo-CRT and outcome in rectal cancer.

Background

Different individuals with renal cell carcinoma (RCC) exhibit substantial heterogeneity in histomorphology, genetic alterations in the proteome, immune cell infiltration patterns, and clinical behavior.

Objectives

This study aims to use single-nucleus sequencing on ten samples (four normal, three clear cell renal cell carcinoma (ccRCC), and three chromophobe renal cell carcinoma (chRCC)) to uncover pathogenic origins and prognostic characteristics in patients with RCC.

Methods

By using two algorithms, inferCNV and k-means, the study explores malignant cells and compares them with the normal group to reveal their origins. Furthermore, we explore the pathogenic factors at the gene level through Summary-data-based Mendelian Randomization and co-localization methods. Based on the relevant malignant markers, a total of 212 machine-learning combinations were compared to develop a prognostic signature with high precision and stability. Finally, the study correlates with clinical data to investigate which cell subtypes may impact patients’ prognosis.

Results & conclusion

Two main origin tumor cells were identified: Proximal tubule cell B and Intercalated cell type A, which were highly differentiated in epithelial cells, and three gene loci were determined as potential pathogenic genes. The best malignant signature among the 212 prognostic models demonstrated high predictive power in ccRCC: (AUC: 0.920 (1-year), 0.920 (3-year) and 0.930 (5-year) in the training dataset; 0.756 (1-year), 0.828 (3-year), and 0.832 (5-year) in the testing dataset. In addition, we confirmed that LYVE1⁺ tissue-resident macrophage and TOX⁺ CD8 significantly impact the prognosis of ccRCC patients, while monocytes play a crucial role in the prognosis of chRCC patients.

A blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.