In search of triple-negative DCIS: tumor-type dependent model of breast cancer progression from DCIS to the invasive cancer

Abstract

This paper is based on the idea that ductal breast cancer in situ (DCIS) precedes the invasive breast cancer (invBC), although the triple-negative invBCs almost lack their DCIS precursor. Reported incidences of breast tumor types in DCIS and in invasive BCs suggest that probabilities of tumor progression might differ among tumor types, and these differences can have some impact on our patients. Reported data from several papers on incidences of the four breast tumor types-luminal A, luminal B, HER2, and triple negative-are used to compare tumor-type incidences for DCIS and for the invasive BC. The pooled distributions differed (Χ (2) = 97.05, p < 0.0001), suggesting a strong selection pressure that reduces the number of triple-negative DCIS lesions at the time of breast tumor diagnosis. Reported shares of DCIS in all newly diagnosed breast cancers range in large screening trials from 9 to 26%, so in making a population model, three values are arbitrarily chosen: one DCIS out of ten breast cancers (the 10% share), one DCIS out of seven breast cancers (one seventh or the 14.3% share), and one out of five (the 20% share). By using these shares and the pooled data of tumor-type incidences, values are calculated that would be expected from a hypothetical population in which types of DCIS and invasive BC are distributed accordingly to the reported incidences. The model predicts that the shares of breast cancer tumor types in the modeled population (DCIS plus invasive BCs) are 39% for luminal A, 20% for luminal B, 11% for HER2 positive, and 30% for the triple-negative cancers. Some 59% of all breast tumors are expected to be hormone receptor positive, and HER2 to be overexpressed in 31%. Simulated probabilities of tumor progression were used to calculate the number of tumor progression t(1/2) that has passed before the time of diagnosis. Calculated relative t(1/2) durations in the modeled population suggest that the triple-negative DCIS cases were fastest in tumor progression, three times faster than the HER2-positive tumors and near twice as fast as luminal A. Luminal A is the model slower than luminal B DCIS, suggesting that although their progression depends on estrogen exposure, HER2 overexpression in luminal B tumors adds some speed in tumor progression. The model results suggest that quick tumor progression might be the main feature of the triple-negative breast tumors, leading to seldom triple-negative DCIS at the time of breast cancer diagnosis. Applying approach of the presented model to the real data from a well-defined population seems warranted.