Association of gBRCA1/2 mutation locations with ovarian cancer risk in Japanese patients from the CHARLOTTE study

Whether germline (g) breast cancer susceptibility gene (BRCA) mutations are located within or outside the ovarian cancer cluster region (OCCR) (1380‐4062 bp for gBRCA1, and between 3249‐5681 bp and 6645‐7471 bp for gBRCA2) may influence risk variations for ovarian cancers. This ad hoc analysis of the CHARLOTTE epidemiological study in Japan assessed the distribution of gBRCA1/2 mutations in patients with newly diagnosed ovarian cancer, and investigated an association between gBRCA1/2 mutation locations and ovarian cancer risk. Differences in patient background and clinical characteristics in subgroups stratified by gBRCA1/2 mutation locations were also evaluated. We analyzed the data of 93 patients (14.7%) from the CHARLOTTE study who were positive for gBRCA1/2 mutations. After excluding 16 cases with L63X founder mutation, 28 (65.1%) of gBRCA1 mutations were within the OCCR. Of 30 gBRCA2 mutations, 15 (50.0%) were within the OCCR. Of 27 patients (one patient excluded for unknown family history) with gBRCA1 mutations located in the OCCR, 11 (40.7%) had a family history of ovarian cancer; the proportion of patients with a family history of ovarian cancer and gBRCA1 mutations outside the OCCR was lower (13.3%). Sixty percent of patients with gBRCA1 mutations outside the OCCR had a family history of breast cancer; the proportion of patients with a family history of breast cancer and gBRCA1 mutations within the OCCR was relatively lower (33.3%). Understanding the mutation locations may contribute to more accurate risk assessments of susceptible individuals and early detection of ovarian cancer among gBRCA mutation carriers.     

Osimertinib in poor performance status patients with T790M-positive advanced non-small-cell lung cancer after progression of first- and second-generation EGFR-TKI treatments (NEJ032B)

Background

Osimertinib is effective in patients with T790M mutation-positive advanced non-small-cell lung cancer (NSCLC) resistant to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). However, its effectiveness and safety in patients with poor performance status (PS) are unknown.

Methods

Enrolled patients showed disease progression after treatment with gefitinib, erlotinib, or afatinib; T790M mutation; stage IIIB, IV, or recurrent disease; and PS of 2–4. Osimertinib was orally administered at a dose of 80 mg/day. The primary endpoint of this phase II study (registration, jRCTs061180018) was response rate and the secondary endpoints were progression-free survival (PFS), overall survival (OS), disease control rate, and safety.

Results

Thirty-three patients were enrolled, of which 69.7% and 24.2% had PS of 2 and 3, respectively. One patient was excluded due to protocol violation; in the remaining 32 patients, the response rate was 53.1%; disease control rate was 75.0%; PFS was 5.1 months; and OS was 10.0 months. The most frequent adverse event of grade 3 or higher severity was lymphopenia (12.1%). Interstitial lung disease (ILD) was observed at all grades and at grades 3–5 in 15.2% (5/33) and 6.1% (2/33) of patients, respectively. Treatment-related death due to ILD occurred in one patient. Patients negative for activating EGFR mutations after osimertinib administration had longer median PFS than those positive for these mutations.

Conclusion

Osimertinib was sufficiently effective in EGFR-TKI-resistant, poor PS patients with T790M mutation-positive advanced NSCLC. Plasma EGFR mutation clearance after TKI treatment could predict the response to EGFR-TKIs.

     

Sarcopenia with inflammation as a predictor of survival in patients with head and neck cancer

ObjectiveAlthough both sarcopenia and systemic inflammation affect the outcomes of head and neck cancer (HNC) patients, the association between sarcopenia and systemic inflammation and the combined prognostic effect of these factors in HNC patients remain unknown. This study aimed to evaluate the effect of sarcopenia with systemic inflammation on survival and disease control in HNC patients.MethodsWe retrospectively reviewed medical records of HNC patients treated between 2009 and 2016. The skeletal muscle area was measured using a single computed tomography image slice at the level of the third cervical vertebra. A prognostic score (SPLR) was developed based on sarcopenia and the platelet-lymphocyte ratio (PLR), and its prognostic value was evaluated.ResultsOverall, 164 patients were enrolled. In the multivariate analysis, sarcopenia was significantly associated with poor overall survival (OS) (p < 0.01). However, neither sarcopenia nor a high PLR was an independent prognostic factor for disease-free survival (DFS) or locoregional recurrence-free survival (LRFS). A high PLR was an independent predictor for sarcopenia (p < 0.01). A high SPLR was associated with older age, lower serum hemoglobin, and lower body mass index (all p < 0.05). Multivariate analysis revealed that SPLR was a significant independent predictor of OS, DFS, and LRFS (all p < 0.05).ConclusionsSystemic inflammation is significantly associated with sarcopenia. The survival and oncological effects of sarcopenia were enhanced when PLR was high. Thus, the combination of these two parameters may be useful for identifying HNC patients at a risk of poor survival outcomes.     

Bcl‐3 induced by IL‐22 via STAT3 activation acts as a potentiator of psoriasis‐related gene expression in epidermal keratinocytes

IL‐22 induces STAT3 phosphorylation and mediates psoriasis‐related gene expression. However, the signaling mechanism leading from pSTAT3 to the expression of these genes remains unclear. We focused on Bcl‐3, which is induced by STAT3 activation and mediates gene expression. In cultured human epidermal keratinocytes, IL‐22 increased Bcl‐3, which was translocated to the nucleus with p50 via STAT3 activation. The increases in CXCL8, S100As and human β‐defensin 2 mRNA expression caused by IL‐22 were abolished by siRNA against Bcl‐3. Although CCL20 expression was also augmented by IL‐22, the knockdown of Bcl‐3 increased its level. Moreover, the combination of IL‐22 and IL‐17A enhanced Bcl‐3 production, IL‐22‐induced gene expression, and the expression of other psoriasis‐related genes, including those encoding IL‐17C, IL‐19, and IL‐36γ. The expression of these genes (except for CCL20) was also suppressed by the knockdown of Bcl‐3. Bcl‐3 overexpression induced CXCL8 and HBD2 expression but not S100As expression. We also compared Bcl‐3 expression between psoriatic skin lesions and normal skin. Immunostaining revealed strong signals for Bcl‐3 and p50 in the nucleus of epidermal keratinocytes from psoriatic skin. The IL‐22‐STAT3‐Bcl‐3 pathway may be important in the pathogenesis of psoriasis.     

Unitary step of gliding machinery in Mycoplasma mobile

Among the bacteria that glide on substrate surfaces, Mycoplasma mobile is one of the fastest, exhibiting smooth movement with a speed of 2.0–4.5 μm⋅s⁻¹ with a cycle of attachment to and detachment from sialylated oligosaccharides. To study the gliding mechanism at the molecular level, we applied an assay with a fluorescently labeled and membrane-permeabilized ghost model, and investigated the motility by high precision colocalization microscopy. Under conditions designed to reduce the number of motor interactions on a randomly oriented substrate, ghosts took unitary 70-nm steps in the direction of gliding. Although it remains possible that the stepping behavior is produced by multiple interactions, our data suggest that these steps are produced by a unitary gliding machine that need not move between sites arranged on a cytoskeletal lattice.The fastest of the Mycoplasma species is Mycoplasma mobile (M. mobile); they glide with a speed of 2.0–4.5 μm⋅s⁻¹ (1, 2). Under an optimal-growth condition, cultivated single M. mobile cells are flask-shaped (Fig. 1A) and glide smoothly across a substrate covered with surface-immobilized sialylated oligosaccharides (3) in the direction of protrusion at a constant speed (Movie S1). Genomic sequencing and analysis have revealed that the mechanism must differ from other forms of motor protein systems and bacterial motility, because M. mobile lacks genes encoding conventional motor proteins in eukaryotes, such as myosin, kinesin, and dynein, in addition to lacking other motility structures in bacteria, such as flagella and pili (4). So far, three proteins have been identified as a part of the gliding machinery (Fig. 1B, Bottom): Gli123 (5), Gli521 (6), and Gli349 (7). The machinery units localize around the cell neck, and their number has been estimated to be ∼450 (2, 5, 8). Gli349 extends out from the cell membrane and shows a rod structure, ∼100 nm in total, with two flexible hinges when isolated (9). Notably, the machinery is driven by hydrolysis of ATP to ADP and inorganic phosphate, caused by an unknown ATPase (10). Because of the large size and characteristic structure of Gli349, and a series of studies with mutants and inhibitory antibodies (2, 11), it has been hypothesized that Gli349 works as a “leg” by binding to and releasing from a substrate covered with randomly arranged sialylated oligosaccharides (2) consuming the chemical energy of ATP. In addition, the pivoting movement of an elongated cell suggests that there are units working not simultaneously but rather independently to propel the cell forward (12). To test this hypothesis and identify conformational changes of a key part of the gliding machinery, we here designed an assay to detect the movement of M. mobile by high precision colocalization microscopy. In the presence of an excess number of binding targets in the solution, which decreased the number of active legs, stepwise displacement was shown for the first time, to our knowledge, to occur in gliding bacteria.Open in a separate windowFig. 1.Nanometer-scale tracking of Mycoplasma gliding. (A) A dark-field image of M. mobile. The image was captured with center-stop optics to maintain the high numerical aperture of the objective, which enabled a high spatial resolution (35). (Scale bar: 1 μm.) (B, Upper) Illustration of the fluorescent ghost. The gliding machinery was distributed around the neck portion, but only the active machinery bound to the glass is shown for simplicity. (Bottom) A construction model of the gliding machinery comprising three proteins: Gli123, Gli521, and Gli349. See the review by Miyata (2) for more detail. (C) A fluorescent image of the labeled ghost was acquired with a time resolution of 2 ms. (Scale bar: 1 μm; pixel size: 240 nm.) (D) The intensity profile of C. The XY area is 5 × 5 μm. (E) Gaussian fitting to D. Nanometer-scale tracking is achieved by positioning the peak of the 2D Gaussian function fitting to the intensity profile of the ghost. (F, Left) The speed of gliding ghosts at different [ATP]s in the solution (n = 129). The cyan curve shows a fit with Michaelis–Menten kinetics; Vmaxspeed and K_m are 2.6 µm⋅s⁻¹ and 61 µM, respectively. The dotted cyan curve shows a fit with the kinetics including the Hill coefficient; Vmaxspeed, [ATP₅₀] and n are 2.2 µm⋅s⁻¹, 43 µM, and 2.4, respectively. (Right) The speed of living cells with no ATP in the solution (2.1 ± 0.1 µm⋅s⁻¹; n = 22). (G) Effect of SL on the gliding velocity of the ghost at saturated [ATP]s, 0.3–1.0 mM (n = 50).