Long-term prognosis of breast cancer detected by mammography screening or other methods

Introduction Previous studies on breast cancer have shown that patients whose tumors are detected by mammography screening have a more favorable survival. However, little is known about the long-term prognostic impact of screen-detection. The purpose of the current study was to compare breast cancer-specific long-term survival between patients whose tumors were detected in mammography screening and those detected by other methods. Methods Breast cancer patients diagnosed within five specified geographical areas in Finland in 1991-92 were identified (n=2,936). Detailed clinical, treatment and outcome data as well as tissue samples were collected. Women with in situ carcinoma, distant metastases at the primary diagnosis and women who were not operated were excluded. Main analyses were made with exclusions of patients with other malignancy or contralateral breast cancer followed by to sensitivity analyses with different exclusion criterias. Median follow-up time was 15.4 years. Univariate and multivariate analysis of breast cancer-specific survival were performed. Results Of patients included in the main analyses (n=1,884) 22% (n=408) were screen-detected and 78% (n=1,476) were detected by other methods. Breast cancer-specific 15-year survival was 86% for patients with screen-detected cancer and 66% for patients diagnosed by other methods (p<0.0001, HR=2.91). Similar differences in survival were also observed in women at screening age (50-69 years) as well as in clinically important subgroups, such as patients with small tumors ([less than or equal to]1cm in diameter) and without nodal involvement (N0). Women with breast cancer diagnosed by screening mammography had a more favorable prognosis compared to those diagnosed outside of screening program following adjustments according to patient age, tumor size, axillary lymph node status, histological grade and hormone receptor status. Significant differences in the risk of having future contralateral breast cancer according to method of detection was not observed . Conclusions Breast cancer detection in mammography screening is an independent prognostic factor in breast cancer and is associated with a more favorable survival also in long-term follow-up.BioMed Central open acces

Low-level alternative tRNA priming of reverse transcription of HIV-1 and SIV in vivo

Abstract Background Reverse transcription (RT) of HIV and SIV is initiated by the binding of the acceptor stem of tRNALys3 to the primer binding site (PBS) of the viral RNA genome. Previous studies have suggested that this tRNALys3 is not the only molecule capable of priming reverse transcription, and that at least one other lysyl tRNA, tRNALys5, which has an acceptor stem sequence varying from tRNALys3 by only a single transition mutation resulting in the integration of a thymine (T) at position 8 of the PBS in the viral genome, can prime reverse transcription. Results We undertook an unbiased approach, evaluating the primer binding site by deep-sequencing of HIV and SIV directly from the plasma of 15 humans and 11 macaques. We found that in humans there are low but measurable levels of viral RNA genomes harboring a PBS containing the noncanonical T at position 8 (PBS-Lys5) corresponding to the tRNAlys5 sequence and representing an average of 0.52% (range 0.07–1.6%) of the total viral population. This value is remarkably consistent with the proportion of PBS-Lys5 we identified in a cross-sectional assessment of the LANL HIV database (0.51%). In macaques chronically infected with SIVmac239, the PBS-Lys5 was also detected but at a frequency 1-log less than seen for HIV, with an average of 0.056% (range 0.01–0.09%). At this proportion, PBS-Lys5 was comparable to other transition mutations, making it impossible to determine whether the mutation observed is a result of use of tRNALys5 as an RT primer at very low levels or merely the product of in vitro cDNA synthesis/PCR error. We also identified two novel PBS sequences in HIV and SIV at low levels in vivo corresponding to tRNALys6 and tRNALys1,2, suggesting that these tRNAs may rarely also be used to prime RT. In vivo reversion of the PBS-Lys5 found in SIVmac239 was rapid and reached background levels by 30 days post-infection. Conclusions We conclude that while alternative tRNAs can initiate reverse transcription of HIV and SIV in vivo, their overall contributions to the replicating viral population are small

Recombination Enhances HIV-1 Envelope Diversity by Facilitating the Survival of Latent Genomic Fragments in the Plasma Virus Population

<div><p>HIV-1 is subject to immune pressure exerted by the host, giving variants that escape the immune response an advantage. Virus released from activated latent cells competes against variants that have continually evolved and adapted to host immune pressure. Nevertheless, there is increasing evidence that virus displaying a signal of latency survives in patient plasma despite having reduced fitness due to long-term immune memory. We investigated the survival of virus with latent envelope genomic fragments by simulating within-host HIV-1 sequence evolution and the cycling of viral lineages in and out of the latent reservoir. Our model incorporates a detailed mutation process including nucleotide substitution, recombination, latent reservoir dynamics, diversifying selection pressure driven by the immune response, and purifying selection pressure asserted by deleterious mutations. We evaluated the ability of our model to capture sequence evolution <i>in vivo</i> by comparing our simulated sequences to HIV-1 envelope sequence data from 16 HIV-infected untreated patients. Empirical sequence divergence and diversity measures were qualitatively and quantitatively similar to those of our simulated HIV-1 populations, suggesting that our model invokes realistic trends of HIV-1 genetic evolution. Moreover, reconstructed phylogenies of simulated and patient HIV-1 populations showed similar topological structures. Our simulation results suggest that recombination is a key mechanism facilitating the persistence of virus with latent envelope genomic fragments in the productively infected cell population. Recombination increased the survival probability of latent virus forms approximately 13-fold. Prevalence of virus with latent fragments in productively infected cells was observed in only 2% of simulations when we ignored recombination, while the proportion increased to 27% of simulations when we allowed recombination. We also found that the selection pressures exerted by different fitness landscapes influenced the shape of phylogenies, diversity trends, and survival of virus with latent genomic fragments. Our model predicts that the persistence of latent genomic fragments from multiple different ancestral origins increases sequence diversity in plasma for reasonable fitness landscapes.</p></div

The effect of recombination on survival of activated latent HIV in the plasma population.

<p>A) Simulations without recombination. B) Simulations with recombination. Grey lines show the proportion of virus with latent genomic fragments in the productively infected cell population of individual simulations, where the bold green line is the mean proportion and the thin green lines outline the 95% confidence envelope. Comparing panels A and B, clearly shows that recombination facilitates survival of latent forms.</p

The divergence and diversity of simulated sequences in the latent reservoir initially increase much more slowly than in productively infected cells.

<p>After approximately 2 years post-PHI, diversity grows linearly in the latent reservoir (blue solid line) while it starts to saturate in plasma (green solid line). Divergence in the latent reservoir (blue dashed line) grows at approximately the same rate as in the plasma (green dashed line) 6 years post-PHI.</p

Tree shape and diversity are influenced by the fitness landscape.

<p>We ordered the simulations based on the average proportion of phylogenetic lineages surviving between samplings, and formed three groups consisting of 50 simulations each, with low (mean 6%), intermediate (mean 32%), and high (mean 69%) survival of lineages, respectively. (A) Typical trees of each group. Branch lengths are according to the indicated scale. Color indicates sampling time. (B) Individual fitness landscapes (grey lines) and average profile (quadratic fit, turquoise lines) of each group. (C) Individual diversity curves (grey lines) and average trends (turquoise lines).</p

Model schematic.

<p>We model interactions between and within three compartments: the latent reservoir, productively infected cells, and the immune response during generation <i>n</i>. The circles represent infected cells (both productively infected and latent), the triangles progeny virus, and the pies (circular sectors) the immune response. The colors of the circles and triangles represent different viral epitopes, and the colors in the pies indicate which viral epitopes are recognized by the immune response. Each productively infected cell produces the same number of infectious virus particles. The population of virus is sampled based on fitness to form the next set of productively infected cells, where the fitness of each virus depends on whether it is recognized by the immune response. If a new viral antigen reaches high enough numbers in the plasma, it triggers an immune response. Upon infection a small fraction of cells becomes latent. To mimic this, we assign a small probability to an infected cell moving to the latent reservoir. Also, cells in the latent reservoir have some probability of being activated and joining the replicating population. The viral sequences in productively infected cells are mutated, mimicking events that occur during reverse transcription, and the two parental strains in dually infected cells have some probability of recombining. Cells in the latent reservoir have some probability of dying, and homeostatically proliferate such that the size of the reservoir is maintained.</p

The divergence and diversity of simulated sequences capture evolutionary trends in clinical data.

<p>Grey lines correspond to the divergence (A) or diversity (B) of the sequences in each simulation, and white lines show mean of simulations. Black lines show real patient data. Note that divergence and diversity were calculated every 30 generations for 15,000 sequences in each of the 1000 simulations.</p

Survival of different latent genomic fragments increases sequence diversity.

<p>We ran 1000 simulations with the intermediate fitness landscape shown in the insert, and categorized the results based on the number of latent genomic fragments from different origins at 1% or greater frequency at 10 years post-PHI. (A) As the number of latent origins increases, so does mean sequence diversity. (B) As the number of latent origins increases, mean sequence divergence decreases.</p

Latent genomic fragment patterns.

<p>(A) The proportion of sequences with latent fragments declines rapidly as the number of latent fragments per sequence increases. (B) The number of latent sites in recombinants decreases as more latent fragments are introduced. (C) As more latent fragments exist in a sequence, they have different origins in time. The grey envelopes indicate 95% of simulation results, white lines indicate the mean, and the black lines the median trends.</p