Biology subjects

Kalinsky, K.

Publications and source records attributed to Kalinsky, K..

3 recordsLinked to original sources

bioRxiv2021

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

Predicting tumor sensitivity to antineoplastics remains an elusive challenge, with no methods demonstrating predictive power. Joint analysis of tumors--from patients with distinct malignancies who had progressed on multiple lines of therapy--and drug perturbation transcriptional profiles predicted sensitivity to 28 of 350 drugs, 26 of which (93%) were confirmed in low-passage, patient-derived xenograft (PDX) models. Drugs were prioritized based on their ability to either invert the activity of individual Master Regulator proteins, with available high-affinity inhibitors, or of the modules they comprise (Tumor-Checkpoints), based on de novo mechanism of action analysis. Of 138 PDX mice enrolled in 16 single and 18 multi-protein treatment arms, a disease control rate (DCR) of 68% and 91 %, and an objective response rate (ORR) of 12% and 17%, were achieved respectively, compared to 6% and 0% in the negative controls arm, with multi-protein drugs achieving significantly more durable responses. Thus, these approaches may effectively complement and expand current precision oncology approaches, as also illustrated by a case study.

systems biology↗

bioRxiv2021

Convergent evolution of resistance pathways during early stage breast cancer treatment with combination cell cycle (CDK) and endocrine inhibitors

Combining cyclin-dependent kinase (CDK) inhibitors with endocrine therapy improves outcomes for metastatic estrogen receptor positive (ER+), HER2 negative, breast cancer patients. However, the value of this combination in potentially curable earlier stage patients is not clear. Using single cell transcriptomic profiling, we examined the evolutionary trajectories of early stage breast cancer tumors using serial tumor biopsies from a clinical trial of preoperative endocrine therapy (letrozole) alone or in combination with the cell cycle inhibitor ribociclib. Applying hierarchical regression and Gaussian process mathematical modelling, we classified each tumor by whether it shrinks or persists with therapy and determined cancer phenotypes related to evolution of resistance and cell cycle transcriptional rewiring. We found that all patients tumors undergo subclonal evolution during therapy, irrespective of the clinical response. However, tumors subjected to endocrine therapy alone showed reduced diversity over time, while those facing combination therapy exhibited increased diversity. Despite different subclonal diversity, single nuclei RNA sequencing uncovered common phenotypic changes in tumor cells that persist following treatment. In these tumors, cancer cells with accelerated loss of estrogen signaling have convergent up-regulation of the JNK pathway, while cells that maintain estrogen signaling during therapy show potentiation of CDK4/6 activation consistent with ERBB4 and ERK signaling up-regulation. These convergent phenotypes were associated with growing tumors resistant to combination therapy. Cell cycle reconstruction identified that these tumors can rebound during combination therapy treatment, indicating stronger selection and promotion of a proliferative state. These results indicate that combination therapy in early stage ER+ breast cancers with ER and CDK inhibition drives rapid evolution of resistance via a shift from estrogen signaling to alternative growth factor receptor mediated proliferation and JNK signaling activation, concordant with a bypass in the G1 checkpoint.

cancer biology↗

bioRxiv2021

Reconstructing tumor trajectories during therapy through integration of multiple measurement modalities.

BackgroundAccurately determining changes in tumor size during therapy is essential to evaluating response or progression. However, individual imaging methodologies often poorly reflect pathologic response and long-term treatment efficacy in patients with estrogen receptor positive (ER+) early-stage breast cancer. Mathematical models that measure tumor progression over time by integrating diverse imaging and tumor measurement modalities are not currently used but could increase accuracy in measuring response and provide biological insights into cancer evolution. MethodsFor ER+ breast cancer patients enrolled on a neoadjuvant clinical trial, we reconstructed their tumor size trajectories during therapy by combining all available information on tumor size, including different imaging modalities, physical examinations and pathological assessment data. Tumor trajectories during six months of treatment were generated, using a Gaussian process and the most probable trajectories were evaluated, based on clinical data, using measurement models that account for biases and differences in precision between tumor measurement methods, such as MRI, ultrasound and mammograms. ResultsReconstruction of tumor trajectories during treatment identified five distinct patterns of tumor size changes, including rebound growth not evident from any single modality. These results increase specificity to distinguish innate or acquired resistance compared to using any single measurement alone. The speed of therapeutic response and extent of subsequent rebound tumor growth quantify sensitivity or resistance in this patient population. ConclusionsTumor trajectory reconstruction integrating multiple modalities of tumor measurement accurately describes tumor progression on therapy and reveals various patterns of patient responses. Mathematical models can integrate diverse response assessments and account for biases in tumor measurement, thereby providing insights into the timing and rate at which resistance emerges.

systems biology↗