Management of postradiation late hemorrhage following treatment for HPV‐positive oropharyngeal squamous cell carcinoma

xlomafota13 shared this article with you from Inoreader Management of postradiation late hemorrhage following treatment for HPV‐positive oropharyngeal squamous cell carcinoma Via Head & Neck Abstract Background Acute oropharyngeal hemorrhage is a serious complication for patients with oropharyngeal squamous cell carcinoma (OPSCC), particularly in patients with a history of radiation therapy (RT). Methods Retrospective case series from at a tertiary care center for treated patients with HPV-positive OPSCC presenting with oropharyngeal hemorrhage. Results Median time from completion of chemoradiation to first hemorrhagic event was 186 days (range 66–1466 days). Seven patients (58%) required intervention to secure their airway. All patients were evaluated for endovascular intervention, six (50%) were embolized. Eight patients (67%) had a second hemorrhagic event; median time to second bleed was 22 days (range 3–90 days). Conclusions Acute oropharyngeal hemorrhage is a sequelae following treatment for HPV-positive OPSCC. The majority of bleeds occurred within a year of completion of treatment. While more research is needed to determine optimal treatment paradigms, endovascular intervention should be considered, even if noninvasive imaging does not demonstrate active bleeding. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

STAMP2 suppresses autophagy in prostate cancer cells by modulating the integrated stress response pathway

xlomafota13 shared this article with you from Inoreader STAMP2 suppresses autophagy in prostate cancer cells by modulating the integrated stress response pathway Via American Journal of Cancer Research Am J Cancer Res. 2022 Jan 15;12(1):327-336. eCollection 2022. ABSTRACT Six Transmembrane Protein of Prostate 2 (STAMP2) is critical for prostate cancer (PCa) growth. We previously showed that STAMP2 regulates the expression of stress induced transcription factor ATF4, which is implicated in starvation-induced autophagy. We therefore investigated whether STAMP2 is involved in the regulation of autophagy in PCa cells. Here we show that STAMP2 suppresses autophagy in PCa cells through modulation of the integrated stress response axis. We also find that STAMP2 regulates mitochondrial respiration. These findings suggest that STAMP2 has significant metabolic effects through mitochondrial function and autophagy, both of which support PCa growth. PMID:35141021 | PMC:PMC8822275 View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Exploiting induced vulnerability to overcome PARPi resistance and clonal heterogeneity in BRCA mutant triple-negative inflammatory breast cancer

xlomafota13 shared this article with you from Inoreader Exploiting induced vulnerability to overcome PARPi resistance and clonal heterogeneity in BRCA mutant triple-negative inflammatory breast cancer Via American Journal of Cancer Research Am J Cancer Res. 2022 Jan 15;12(1):337-354. eCollection 2022. ABSTRACT Acquired resistance and clonal heterogeneity are critical challenges in cancer treatment, and the lack of effective computational tools hampers the discovery of new treatments to overcome resistance. Using high-throughput transcriptomic databases of compound perturbation profiles, we have developed a bioinformatic strategy for identifying candidate drugs to overcome resistance with combinatorial therapy. We devised this strategy during an investigation into the acquired resistance against PARP inhibitors (PARPi) in a triple-negative inflammatory breast cancer cell line. In this study, we derived multiple PARPi-resistant clones and characterized their transcriptomic adaptations compared to the parental clone. The transcriptomes of the resistant clones showed substantial heterogeneity, highlighting the importance of characterizing multiple clones from the same tumour. Surprisingly, we found that these transcriptomic changes may not actually confer PARPi resistance, but they may nevertheless induce a shared secondary vulnerability. By modeling our data in relation to transcriptomic perturbation profiles of compounds, we uncovered deficiencies in Ras signaling that resulted from transcriptional adaptation to long-term PARPi treatment across multiple resistant clones. Due to these induced deficiencies, we predicted that the resistant clones would be sensitive to pharmacological reinforcement of PARPi-induced transcriptional adaptation. We then experimentally validated this predicted vulnerability that is shared by multiple resistant clones. Our results thus provide a promising paradigm for integrating transcriptomic data with compound perturbation profiles in order to identify drugs that can exploit an induced vulnerability and overcome therapeutic resistance, thus providing another strategy towards precision oncology. PMID:35141022 | PMC:PMC8822293 View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

De-glycosylated membrane PD-L1 in tumor tissues as a biomarker for responsiveness to atezolizumab (Tecentriq) in advanced breast cancer patients

xlomafota13 shared this article with you from Inoreader De-glycosylated membrane PD-L1 in tumor tissues as a biomarker for responsiveness to atezolizumab (Tecentriq) in advanced breast cancer patients Via American Journal of Cancer Research Am J Cancer Res. 2022 Jan 15;12(1):123-137. eCollection 2022. ABSTRACT The atezolizumab (Tecentriq), a humanized antibody against human programmed death ligand 1 (PD-L1), combined with nab-paclitaxel was granted with accelerated approval to treat unresectable locally advanced or metastatic triple-negative breast cancer (TNBC) due to the encouraging positive results of the phase 3 IMpassion130 trial using PD-L1 biomarker from immune cells to stratify patients. However, the post-market study IMpassion131 did not support the original observation, resulting in the voluntary withdrawal of atezolizumab from the indication in breast cancer by Genentech in 2021. Emerging evidence has revealed a high frequency of false negative result using the standard immunohistochemical (IHC) staining due to heavy glycosylation of PD-L1. The removal of glycosylation prevents from the false negative staining, enabling more accurate assessment of PD-L1 levels and improving prediction for response to immune checkpoint therapy. In the present study, the natural and de-glycosylated PD-L1 expression in tumor and immune cells from nine TNBC patients were analyzed by using clone 28-8 monoclonal antibody to correlate with treatment outcome. Our results demonstrate that: (1) Removal of the glycosylation indeed enhances the detection of PD-L1 by IHC staining, (2) The PD-L1 levels on tumor cell surface after removal of the glycosylation correlates well with clinical responses for atezolizumab treatment; (3) The criteria used in the IMpassion130 and IMpassion131 trials which scored the natural PD-L1 in the immune cells failed to correlate with the clinical response. Taken together, tumor cell surface staining of PD-L1 with de-glycosylation has a significant correlation with the clinical response for atezolizumab treatment, suggesting that treatment of atezolizumab may be worthy of further consideration with de-glycosylation procedure as a patient s tratification strategy. A larger cohort to validate this important issue is warranted to ensure right patient population who could benefit from the existing FDA-approved drugs. PMID:35141008 | PMC:PMC8822291 View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Artificial intelligence for early diagnosis of lung cancer through incidental nodule detection in low- and middle-income countries-acceleration during the COVID-19 pandemic but here to stay

xlomafota13 shared this article with you from Inoreader Artificial intelligence for early diagnosis of lung cancer through incidental nodule detection in low- and middle-income countries-acceleration during the COVID-19 pandemic but here to stay Via American Journal of Cancer Research Am J Cancer Res. 2022 Jan 15;12(1):1-16. eCollection 2022. ABSTRACT Although the coronavirus disease of 2019 (COVID-19) pandemic had profound pernicious effects, it revealed deficiencies in health systems, particularly among low- and middle-income countries (LMICs). With increasing uncertainty in healthcare, existing unmet needs such as poor outcomes of lung cancer (LC) patients in LMICs, mainly due to late stages at diagnosis, have been challenging-necessitating a shift in focus for judicious health resource utilization. Leveraging artificial intelligence (AI) for screening large volumes of pulmonary images performed for noncancerous reasons, such as health checks, immigration, tuberculosis screening, or other lung conditions, including but not limited to COVID-19, can facilitate easy and early identification of incidental pulmonary nodules (IPNs), which otherwise could have been missed. AI can review every chest X-ray or computed tom ography scan through a trained pair of eyes, thus strengthening the infrastructure and enhancing capabilities of manpower for interpreting images in LMICs for streamlining accurate and early identification of IPNs. AI can be a catalyst for driving LC screening with enhanced efficiency, particularly in primary care settings, for timely referral and adequate management of coincidental IPN. AI can facilitate shift in the stage of LC diagnosis for improving survival, thus fostering optimal health-resource utilization and sustainable healthcare systems resilient to crisis. This article highlights the challenges for organized LC screening in LMICs and describes unique opportunities for leveraging AI. We present pilot initiatives from Asia, Latin America, and Russia illustrating AI-supported IPN identification from routine imaging to facilitate early diagnosis of LC at a potentially curable stage. PMID:35141002 | PMC:PMC8822269 View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Association of machine learning ultrasound radiomics and disease outcome in triple negative breast cancer

xlomafota13 shared this article with you from Inoreader Association of machine learning ultrasound radiomics and disease outcome in triple negative breast cancer Via American Journal of Cancer Research Am J Cancer Res. 2022 Jan 15;12(1):152-164. eCollection 2022. ABSTRACT Triple negative breast cancer (TNBC) is a breast cancer subtype with unfavorable prognosis. We aimed to establish a machine learning-based ultrasound radiomics model to predict disease-free survival (DFS) in TNBC. Invasive TNBC>T1b between January 2009 and June 2018 with preoperative ultrasound were enrolled and assigned to training and independent test cohort. Radiomics and clinicopathological features related with DFS were selected by univariate and multivariate regression analysis. Training cohort of combined features was resampled with SMOTEENN to balance distribution and put into classifiers. Areas Under Curves (AUCs) of models were compared by DeLong's test. 562 women were included with 68 DFS events observed. Twenty prognostic radiomics features were extracted. Machine learning model by Naïve Bayes combining radiomics, clinicopathological features, and SM OTEENN had an AUC of 0.86 (95% CI 0.84-0.88), with sensitivity of 74.7% and specificity of 80.1% in training cohort. In independent test cohort, this three-combination model delivered an AUC of 0.90 (95% CI 0.83-0.95), higher than models based on radiomics (AUC=0.69, P=0.016) or radiomics + SMOTEENN (AUC=0.73, P=0.019). Integrating machine learning radiomics model based on ultrasound and clinicopathological features can predict DFS events for TNBC patients. PMID:35141010 | PMC:PMC8822271 View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

Novel insights linking BRCA1-IRIS role in mammary gland development to formation of aggressive PABCs: the case for longer breastfeeding

xlomafota13 shared this article with you from Inoreader Novel insights linking BRCA1-IRIS role in mammary gland development to formation of aggressive PABCs: the case for longer breastfeeding Via American Journal of Cancer Research Am J Cancer Res. 2022 Jan 15;12(1):396-426. eCollection 2022. ABSTRACT Pregnancy-associated breast cancer (PABC) is diagnosed during or shortly after pregnancy. Although rare, PABC is a serious occurrence often of the triple negative (TNBC) subtype. Here we show progesterone, prolactin, and RANKL upregulate BRCA1-IRIS (IRIS) in separate and overlapping subpopulations of human mammary epithelial cell lines, which exacerbates the proliferation, survival, and the TNBC-like phenotype in them. Conversely, vitamin D3 reduces IRIS expression in TNBC cell lines, which attenuates growth, survival, and the TNBC-like phenotype in them. In the mouse, Brca1-Iris (Iris, mouse IRIS homolog) is expressed at low-level in nulliparous mice, increases ~10-fold in pregnant/lactating mice, to completely disappear in involuting mice, and reappears at low-level in regressed glands. Mice underwent 3 constitutive pregnancies followed by a forced invo lution (after 5 days of lactation) contained ~10-fold higher Iris in their mammary glands compared to those underwent physiological involution (after 21 days of lactation). While protein extracts from lactating glands promote proliferation in IRISlow and IRIS overexpressing (IRISOE) cells, extracts from involuting glands promote apoptosis in IRISlow, and aneuploidy in IRISOE cells. In a cohort of breast cancer patients, lack of breastfeeding was associated with formation of chemotherapy resistant, metastatic IRISOE breast cancers. We propose that terminal differentiation triggered by long-term breastfeeding reduces IRIS expression in mammary cells allowing their elimination by the inflammatory microenvironment during physiological involution. No/short-term breastfeeding retains in the mammary gland IRISOE cells that thrive in the inflammatory microenvironment during forced involution to become precursors for aggressive breast cancers shortly after pregnancy. PMID:35141026 | PMC:PMC8822284 View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.