Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.

Long-term follow-up of complex oncoplastic breast-conserving surgery, standard breast conservation and skin-sparing mastectomy in DCIS - a register-based study.

BACKGROUND: Few studies evaluate oncological safety in complex oncoplastic breast-conserving surgery(C-OBCS) for DCIS. It still needs to be defined whether it is equivalent to standard breast conservation(S-BCS) or an alternative to skin-sparing mastectomy(SSM). This study compares local recurrence rates(LR), disease-free survival(DFS) and overall survival (OS) between the three surgical techniques. METHODS: We conducted a retrospective register-based study on LR, DFS and OS of patients operated with S-BCS(n=1388), C-OBCS (n=106) or skin-sparing mastectomy (n=218) for DCIS diagnosed 2007-2020. Data was extracted from the Norwegian Breast Cancer Registry. RESULTS: In the S-BCS, C-OBCS and SSM groups, median age was 60, 58 and 51 years (p<0.001), median size 15, 25, and 40 mm (p<0.001) and median follow-up 55, 48 and 76 months. At ten years, the overall LR was 12.7%, 14.3% for S-BCS, 11.2% for C-OBCS and 6.8% for SSM. Overall DFS at ten years was 82.3%, 80.5% for S-BCS, 82.4% for C-OBCS and 90.4% for SSM. At ten years, the OS was 93.8%, 93.0% in S-BCS, 93.3% in C-OBCS and 96.6% in the SSM group. Weighted Kaplan Meier plots showed that SSM had a significantly higher DFS than S-BCS (p=0.003) and C-OBCS (p=0.029). DFS in C-OBCS versus S-BCS and the difference in OS was not significant (p=0.264). CONCLUSION: SSM had a significantly higher DFS than S-BCS and C-OBCS. The difference in DFS between S-BCS and C-OBCS, and OS between the three groups was not statistically significant. Our study suggests that C-OBCS is a safe alternative to S-BCS and SSM.

Oncological outcomes after simple and skin-sparing mastectomy of ductal carcinoma in situ: A register-based cohort study of 576 Norwegian women.

BACKGROUND: For Ductal Carcinoma in Situ (DCIS), recurrence is shown to be higher after skin-sparing (SSM) versus simple (SM) mastectomy. This study aimed to compare the two groups recurrence rates, disease-free survival (DFS), and overall (OS) survival. METHODS: We conducted a retrospective register-based cohort study of women operated with SSM (n = 338) or SM (n = 238) for DCIS between 2007 and 2017. Data from the Norwegian Breast Cancer Registry was used to estimate recurrences rates, DFS and OS. RESULTS: Mean age was 51 and 61 years in the SSM and SM groups, respectively. Median follow-up time was 77 months for SSM (range: 21-152 months) vs 84 months for SM (range: 7-171 months). After five years of follow-up, the overall recurrence rate (OR) was 2.1%; 3.9% for SSM and 0.9% for SM. After ten years, the rates were 3.0%, 6.2% for SSM and still 0.9% for SM. DFS was after ten years 92.2%; 91.8% for SSM, and 92.4% for SM. OS was 95.0%; 97.5% for SSM and 93.3% for SM at ten years. For SSM, involved margins represented a significant risk for recurrence. CONCLUSION: The recurrence rate was higher in the SSM versus the SM group. Whether the difference is due to the operating procedures or underlying risk factors remains unknown. When stratifying for the difference in age, there was no statistical difference in DFS or OS. Involved margins in the SSM group were associated with an increased risk of recurrence.

Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data.

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.