Notes

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variants of uncertain significance, follow-up is recommended every 2 years, as actionable reclassifications may happen during this period.
Leptomeningeal disease (LMD) in epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma is associated with a poor prognosis and limited treatment options. Osimertinib is a potent third-generation EGFR tyrosine kinase inhibitor with confirmed CNS penetration. This study reports on outcomes of patients with EGFR-mutated non-small-cell lung cancer who developed LMD and were subsequently treated with osimertinib.

We identified patients treated with osimertinib 80 mg PO daily under a compassionate access scheme across nine tertiary Australian institutes between July 2017 and July 2020. Patient demographics, tumor characteristics, and treatment history were collected. Median overall survival, median progression-free survival, disease control rates (DCR), and overall response rates (ORR) were assessed. Kaplan-Meier analysis was performed and descriptive statistics were used.

Thirty-nine patients were analyzed of which 74% were female. Exon 19 deletions (49%) and L858R point mutations (41%) were the most common EGFR mutations. Forty-nine percentage of patients were Eastern Cooperative Oncology Group 1. The median duration of osimertinib therapy was 6 months. The extracranial DCR and ORR were 60% and 54%, and the intracranial DCR and ORR were 68% and 53%, respectively. Median overall survival was 10.5 months (95% CI, 8.17 to 15.05 months).

There are limited treatment options for LMD in EGFR-positive lung cancer, and osimertinib at a dose of 80 mg daily is an active therapeutic option for these patients.
There are limited treatment options for LMD in EGFR-positive lung cancer, and osimertinib at a dose of 80 mg daily is an active therapeutic option for these patients.
The molecular subtype of breast cancer is an important component of establishing the appropriate treatment strategy. selleck compound In clinical practice, molecular subtypes are determined by receptor expressions. In this study, we developed a model using deep learning to determine receptor expressions from mammograms.

A developing data set and a test data set were generated from mammograms from the affected side of patients who were pathologically diagnosed with breast cancer from January 2006 through December 2016 and from January 2017 through December 2017, respectively. The developing data sets were used to train and validate the DL-based model with five-fold cross-validation for classifying expression of estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2-neu (HER2). The area under the curves (AUCs) for each receptor were evaluated with the independent test data set.

The developing data set and the test data set included 1,448 images (997 ER-positive and 386 ER-negative, 641 PgR-positive and 695 PgR-negative, and 220 HER2-enriched and 1,109 non-HER2-enriched) and 225 images (176 ER-positive and 40 ER-negative, 101 PgR-positive and 117 PgR-negative, and 53 HER2-enriched and 165 non-HER2-enriched), respectively. The AUC of ER-positive or -negative in the test data set was 0.67 (0.58-0.76), the AUC of PgR-positive or -negative was 0.61 (0.53-0.68), and the AUC of HER2-enriched or non-HER2-enriched was 0.75 (0.68-0.82).

The DL-based model effectively classified the receptor expressions from the mammograms. Applying the DL-based model to predict breast cancer classification with a noninvasive approach would have additive value to patients.
The DL-based model effectively classified the receptor expressions from the mammograms. Applying the DL-based model to predict breast cancer classification with a noninvasive approach would have additive value to patients.Development of high-throughput technologies helped to decipher tumor genomic landscapes revealing actionable molecular alterations. We aimed to rank the level of evidence of recurrent actionable molecular alterations in head and neck squamous cell carcinoma (HNSCC) on the basis of the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of Molecular Targets (ESCAT) to help the clinicians prioritize treatment. We identified actionable alterations in 33 genes. HRAS-activating mutations were ranked in tier IB because of the efficacy of tipifarnib (farnesyltransferase inhibitor) in HRAS-mutated patients with HNSCC (nonrandomized clinical trial). Microsatellite instability (MSI), high tumor mutational burden (TMB), and NTRK fusions were ranked in tier IC because of PD-1 and TRK tyrosine kinase inhibitors tissue-agnostic approvals. CDKN2A-inactivating alterations and EGFR amplification were ranked in tier IIA because of the efficacy of palbociclib (CDK4/6 inhibitor) and afatinib (tyrosine kinase inhibitor) in these respective molecular subgroups in retrospective analyses of clinical trials. Molecular alterations in several genes, including PIK3CA gene, were ranked in tier IIIA because of clinical benefit in other tumor types, whereas molecular alterations in IGF1R and TP53 genes were ranked in tier IVA and tier V, respectively. The most compelling actionable molecular alterations in HNSCC according to ESCAT include HRAS-activating mutations, MSI, high TMB, NTRK fusions, CDKN2A-inactivating alterations, and EGFR amplification.
Immunotherapy has been approved to treat many tumor types. However, one characteristic of this therapeutic class is that survival benefit is due to late immune response, which leads to a delayed treatment effect. Quantifying the benefit, if any, of such treatment, will thus require other metrics than the usual hazard ratio and different approaches have been proposed to quantify the long-term response of immunotherapy.

In this paper, we suggest to use quantile regression for survival data to quantify the long-term benefit of immunotherapy. Our motivation is that this approach is not trial-specific and provides clinically understandable results without specifying arbitrary time points or the necessity to reach median survival, as is the case with other methods. We use reconstructed data from published Kaplan-Meier curves to illustrate our method.

On average, patients from the immunotherapy group have 60% chance to survive 5.46 months (95% CI, 2.57 to 9.02) more than patients in the chemotherapy group.
On average, patients from the immunotherapy group have 60% chance to survive 5.46 months (95% CI, 2.57 to 9.02) more than patients in the chemotherapy group.
-mutated (

) non-small-cell lung cancer (NSCLC) is emerging as a heterogeneous disease defined by comutations, which may confer differential benefit to PD-(L)1 immunotherapy. In this study, we leveraged computational biological modeling (CBM) of tumor genomic data to identify PD-(L)1 immunotherapy sensitivity among

NSCLC molecular subgroups.

In this multicohort retrospective analysis, the genotype clustering frequency ranked method was used for molecular clustering of tumor genomic data from 776 patients with

NSCLC. These genomic data were input into the CBM, in which customized protein networks were characterized for each tumor. The CBM evaluated sensitivity to PD-(L)1 immunotherapy using three metrics programmed death-ligand 1 expression, dendritic cell infiltration index (nine chemokine markers), and immunosuppressive biomarker expression index (14 markers).

Genotype clustering identified eight molecular subgroups and the CBM characterized their shared cancer pathway characteristics

apy sensitivity.
CBM identified distinct PD-(L)1 immunotherapy sensitivity among molecular subgroups of KRASMUT NSCLC, in line with previous literature. These data provide proof-of-concept that computational modeling of tumor genomics could be used to expand on hypotheses from clinical observations of patients receiving PD-(L)1 immunotherapy and suggest mechanisms that underlie PD-(L)1 immunotherapy sensitivity.As germline predisposition to hematopoietic malignancies has gained increased recognition and attention in the field of oncology, it is important for clinicians to use a systematic framework for the identification, management, and surveillance of patients with hereditary hematopoietic malignancies (HHMs). In this article, we discuss strategies for identifying individuals who warrant diagnostic evaluation and describe considerations pertaining to molecular testing. Although a paucity of prospective data is available to guide clinical monitoring of individuals harboring pathogenic variants, we provide recommendations for clinical surveillance based on consensus opinion and highlight current advances regarding the risk of progression to overt malignancy in HHM variant carriers. We also discuss the prognosis of HHMs and considerations surrounding the utility of allogeneic stem-cell transplantation in these individuals. link2 We close with an overview of contemporary issues at the intersection of HHMs and precision oncology.
Treatment guidelines for advanced non-small-cell lung cancer (aNSCLC) recommend broad molecular profiling for targeted therapy selection. This study prospectively assessed comprehensive next-generation sequencing (NGS) of cell-free circulating tumor DNA (cfDNA) compared with standard-of-care (SOC) tissue-based testing to identify guideline-recommended alterations in aNSCLC.

Patients with treatment-naïve aNSCLC were tested using a well-validated NGS cfDNA panel, and results were compared with SOC tissue testing. The primary objective was noninferiority of cfDNA vs. tissue analysis for the detection of two guideline-recommended biomarkers (
and
) and an additional six actionable biomarkers. Secondary analyses included tissue versus cfDNA biomarker discovery, overall response rate (ORR), progression-free survival (PFS) to targeted therapy, and positive predictive value (PPV) of cfDNA.

The primary objective was met with cfDNA identifying actionable mutations in 46 patients versus 48 by tissue (
< .cting aNSCLC-recommended biomarkers. link3 Furthermore, cfDNA-based first-line therapy produced outcomes similar to tissue-based testing, demonstrating the clinical utility of comprehensive cfDNA genotyping as the initial genotyping modality in patients with treatment-naïve aNSCLC when tissue is insufficient or when all actionable biomarkers cannot be rapidly assessed.
Phase I trials are a crucial step in the evaluation of new cancer therapies. Historically, low rates of response (5%) and comparably high rates of death from toxicities (0.5%) have contributed to debates on the ethics and orientation of these trials. With the introduction of novel targeted therapies, a contemporary estimate is needed.

We systematically searched PubMed, Embase, and ClinicalTrials.gov for reports of phase I oncology trials of single-agent targeted immunomodulators, molecularly targeted therapies, and antiangiogenic agents, published between January 2015 and July 2018. Adult and pediatric trials of solid and hematological malignancies were eligible. Treatment-related adverse events (grades 3, 4, and 5) and response rates (objective, complete, and partial) were extracted and analyzed.

One hundred and fifty-eight trial reports, covering 6,707 patients, were included. The rate of treatment-related deaths was 0.0% (95% CI, 0.0 to 0.1), while 13.2% of patients (9.5 to 17.3) experienced a grade 3 or 4 treatment-related toxicity.
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