DNA repair deficiency (DRD) is an important driver of carcinogenesis and an efficient target for anti-tumor therapies to improve patient survival. Thus, detection of DRD in tumors is paramount. Currently, determination of DRD in tumors is dependent on wet-lab assays. Here we describe an efficient machine learning algorithm which can predict DRD from histopathological images. The utility of this algorithm is demonstrated with data obtained from 1445 cancer patients. Our method performs rather well when trained on breast cancer specimens with homologous recombination deficiency (HRD), AUC (area under curve) = 0.80. Results for an independent breast cancer cohort achieved an AUC = 0.70. The utility of our method was further shown by considering the detection of mismatch repair deficiency (MMRD) in gastric cancer, yielding an AUC = 0.81. Our results demonstrate the capacity of our learning-base system as a low-cost tool for DRD detection.
Introduction Lymph node fine needle aspiration (LN‐FNA) is a minimally invasive method of evaluating lymphadenopathy. Nonetheless, its use is not widely accepted due to the lack of guidelines and a cytopathological categorisation that directly relates to management. We report our experience with LN FNA at a large Cancer Center in Latin America. Methods We retrospectively collected cytological cases of lymph node FNA from the department of pathology at AC Camargo Cancer Center performed over a 2‐year period. Data extracted included LN location, age, sex and final cytological diagnosis. Patients that had undergone neoadjuvant chemotherapy and/or cases for which the surgery specimen location was not clearly reported were excluded. For those cases with surgical reports, risk of malignancy was calculated for each diagnostic category, along with overall performance of cytology. False positive cases were reviewed to assess any possible misinterpretation or sampling errors. Results A total of 1730 LN‐FNA were distributed as follows: 62 (3.5%) non‐diagnostic (ND); 1123 (64.9%) negative (NEG), 19 (1.1%) atypical (ATY), 53 (3.1%) suspicious for malignancy (SUS), and 473 (27.3%) positive (POS). Surgical reports were available for 560 cases (32.4%). Risk of malignancy (ROM) for each category was 33.3% for ND, 29.9% for NEG, 25% for ATY, 74.2% for SUS and 99.6% for POS. Overall sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were 78.5%, 99.4%, 70.2% and 99.6%, respectively. Conclusion Lymph node FNA is a very specific and accurate exam, which is reliable in the detection of lymph node metastasis and other causes of lymphadenopathy.
Objective: To investigate the diagnostic and prognostic role of gastric fluid DNA (gfDNA) in gasric cancer (GC) patients and controls submitted to upper digestive endoscopy. Design: The concentration of gfDNA was evaluated in 941 samples, including subjects with normal gastric mucosa (n = 10), peptic diseases (n = 596), pre-neoplastic conditions (n = 99), and cancer (n = 236). gfDNA levels were evaluated according to age, gender, BMI, gastric fluids pH, use of proton-pump inhibitors, GC tumor subtypes, histological grades, clinical stages, and disease progression/outcome. Results: In the non-cancer group, we observed that gfDNA levels are increased in women as compared to men (p=7.44e-4). Remarkably, gfDNA levels are increased in GC patients as compared to non-GC (normal + peptic diseases, p=5.67e-13) and in GC versus pre-neoplastic disease (p=1.53e-6). Similar differences were also seen when more advanced tumors (T3) were compared to early stages (T2 and below) (p=5.97-4). Moreover, our results suggest the prognostic value of gfDNA as GC-patients with higher gfDNA concentrations (<1.28ng/microliter) had increased infiltration of immune cells in the tumor (p=1.06e-3), which parallels with better disease-free survival (p= 0.014). Conclusion: These findings highlight the significance of collecting and studying stomach fluids from gastric cancer patients and reveals the potential impact of this approach as well as its diagnostic and prognostic value for disease management.
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