Biology subjects

Ostergaard, J.

Publications and source records attributed to Ostergaard, J..

3 recordsLinked to original sources

bioRxiv2026

ABCB1-Mediated Drug Efflux Drives Resistance to VpreB1-Targeted Antibody-Drug Conjugates in B-cell Lymphoblastic Leukemia

Although B-cell acute lymphoblastic leukemia (B-ALL) is highly responsive to antigen-directed immunotherapies, treatment resistance remains a major barrier to achieving durable responses in patients. We recently developed a novel VpreB1 (CD179a)-directed antibody-drug conjugate with calicheamicin (VpreB1-ADC) that exploits the restricted expression of VpreB1 within the surrogate light chain in early B cells, including B-ALL. In the present work, we investigated mechanisms of resistance to the VpreB1-ADC. Mechanisms of resistance were evaluated using a TCF3::HLF B-ALL model, assessing target engagement parameters including VpreB1 surface expression and antibody internalization. The role of the multidrug resistance transporter ABCB1 (P-glycoprotein) was evaluated via pharmacologic inhibition, using tariquidar and zosuquidar, and enforced overexpression across multiple B-ALL cell lines. Sensitivity to alternative non-ABCB1 substrate payloads exatecan and PNU-159682 was also assessed. Resistant TCF3::HLF cells retained VpreB1 expression and efficient antibody internalization. Instead, resistance was driven by elevated ABCB1 expression and activity. ABCB1 inhibition with tariquidar or zosuquidar restored VpreB1-ADC sensitivity. Conversely, enforced ABCB1 overexpression conferred ADC resistance, which was reversed by ABCB1 inhibition. Cells with high ABCB1 activity remained fully sensitive to alternative payloads, including exatecan and PNU-159682, which are not ABCB1 substrates. ABCB1-mediated drug efflux drives intrinsic resistance to calicheamicin-conjugated ADCs in B-ALL. Combining ADCs with ABCB1 inhibitors or selecting payloads non-susceptible to ABCB1 efflux offer viable strategies to overcome resistance and optimize future ADC therapies.

cancer biology↗

bioRxiv2026

Neural Correlates of Listening States, Cognitive Load, and Selective Attention in an Ecological Multi-Talker Scenario

This study assessed neural responses to continuous speech to classify listening state, cognitive load, and selective auditory attention in complex acoustic environments. EEG was recorded while participants listened to concurrent male and female talkers under two conditions: active listening, where attention was directed to one of two competing speakers (target vs. masker), or passive listening, where attention was diverted to a visual task. Cognitive load was varied by manipulating target-to-masker (TMR) ratio (TMR: +7 dB, -7 dB), with lower TMR representing more demanding listening conditions. Spectral EEG features across frequency bands were ranked with univariate statistics and used to classify listening state (active vs passive) and cognitive load (low vs. high TMR). Auditory attention decoding (AAD) was performed using linear stimulus reconstruction to identify the target talker during active listening. Classification of listening state achieved 90.3% accuracy, and AAD reached 84.4% accuracy, demonstrating robust tracking of attentional engagement. In contrast, classification of cognitive load was near chance, suggesting that more extreme acoustic manipulations may be required to elicit distinct neural signatures. Comparable performance using a reduced set of electrodes near the ear indicates the potential for integration with wearable hearing devices. Overall, these results demonstrate that EEG can distinguish attentional states and selectively track target speech in realistic auditory scenarios. The findings provide a foundation for future applications in monitoring listening behavior, supporting auditory processing, and improving brain-controlled hearing aids in complex acoustic environments. HighlightsO_LIListening state (active vs. passive) can be classified from EEG spectral features. C_LIO_LIAttended speech can be decoded by reconstructing speech envelopes from EEG. C_LIO_LIComparable accuracy is achieved using only electrodes placed around the ears. C_LIO_LIEEG can monitor listening state and track auditory attention in two-speaker settings. C_LI Graphical AbstractEEG signals were recorded while participants listened to two concurrent speech streams, either by actively attending to one speaker or by focusing on an unrelated visual task. Spectral features of the EEG were used to classify listening state (active vs. passive) and cognitive load (low vs. high TMR). Auditory attention decoding (AAD) was performed by reconstructing the speech envelope from the EEG time signal. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/711289v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1079628org.highwire.dtl.DTLVardef@1135404org.highwire.dtl.DTLVardef@1f0d950org.highwire.dtl.DTLVardef@14b4c9a_HPS_FORMAT_FIGEXP M_FIG C_FIG Classification of listening state (active vs. passive): 90.3% accuracy. EEG difference between active and passive listening. Left, power spectrum, right, topographic map (alpha band 8-12 Hz). Classification of cognitive load (low vs high TMR): near chance level. EEG difference between low and high TMR. Left, power spectrum, right, topographic map (alpha band 8-12 Hz). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/711289v1_ufig2.gif" ALT="Figure 2"> View larger version (34K): org.highwire.dtl.DTLVardef@9229b1org.highwire.dtl.DTLVardef@1ef394corg.highwire.dtl.DTLVardef@9adecforg.highwire.dtl.DTLVardef@199f8c2_HPS_FORMAT_FIGEXP M_FIG C_FIG AAD achieved 84.4% accuracy, indicating robust decoding of the attended speaker during active listening, while performance dropped to near chance during passive listening.

neuroscience↗

bioRxiv2023

Phase-locking of neural activity to the envelope of speech in the delta frequency band reflects differences between word lists and sentences

The envelope of a speech signal is tracked by neural activity in the cerebral cortex. The cortical tracking occurs mainly in two frequency bands, theta (4 - 8 Hz) and delta band (1 - 4 Hz). Tracking in the faster theta band has been mostly associated with lower-level acoustic processing, such as the parsing of syllables, whereas the slower tracking in the delta band relates to higher-level linguistic information of words and word sequences. However, much regarding the more specific association between cortical tracking and acoustic as well as linguistic processing remains to be uncovered. Here we recorded electroencephalographic (EEG) responses to both meaningful sentences as well as random word lists in different levels of signal-to-noise ratios (SNRs) that lead to different levels of speech comprehension as well as listening effort. We then related the neural signals to the acoustic stimuli by computing the phase-locking value (PLV) between the EEG recordings and the speech envelope. We found that the PLV in the delta band increases with increasing SNR for sentences but not for the random word lists, showing that the PLV in this frequency band reflects linguistic information. When attempting to disentangle the effects of SNR, speech comprehension, and listening effort, we observed a trend that the PLV in the delta band might reflect listening effort rather than the other two variables, although the effect was not statistically significant. In summary, our study shows that the PLV in the delta band reflects linguistic information and might be related to listening effort.

neuroscience↗